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Harnessing noise in optical computing for AI

A multi-institutional, interdisciplinary research team led by UW ECE Professor Mo Li has found innovative ways of using noise inherent to integrated optoelectronics to enhance the creativity of artificial intelligence.

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UW researchers developing miniaturized imaging device to treat heart attack, stroke

An interdisciplinary research team at the University of Washington (UW), led by Arka Majumdar, was awarded $3.6 million in funding from the National Science Foundation (NSF) to develop a miniaturized imaging device to treat heart attack and stroke.

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UW researchers developing miniaturized imaging device to treat heart attack, stroke Banner

James Rosenthal receives Yang Research Award

UW ECE congratulates recent graduate James Rosenthal, who was named as the 2021 Yang Research Award recipient for his work developing neurotechnology.

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James Rosenthal receives Yang Research Award Banner

The Integrator 2021 - Focus on Impact - now available!

Read this year's issue of The Integrator — UW ECE's flagship, annual magazine highlighting the Department's extraordinary faculty research, student achievements, alumni stories, special events and more!

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The Integrator 2021 - Focus on Impact - now available! Banner

Professor Joshua R. Smith elected Fellow of the National Academy of Inventors for his innovations in wireless power, communication, sensing and robotics

UW ECE and CSE Professor Joshua R. Smith was elected into the 2021 class of Fellows of the National Academy of Inventors for his impactful creations in the fields of wireless power, communication, sensing and robotics.

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Professor Joshua R. Smith elected Fellow of the National Academy of Inventors for his innovations in wireless power, communication, sensing and robotics Banner

Researchers shrink camera to the size of a salt grain

Researchers at the University of Washington and Princeton University have created micro-sized cameras that produce the highest-quality images and widest field of view for full-color metasurface cameras to date, with great potential to spot problems in the human body and provide sensing capabilities for super-small robots.

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https://www.ece.uw.edu/spotlight/using-noise-optical-computing-for-ai/
Harnessing noise in optical computing for AI

Harnessing noise in optical computing for AI

A multi-institutional, interdisciplinary research team led by UW ECE Professor Mo Li has found innovative ways of using noise inherent to integrated optoelectronics to enhance the creativity of artificial intelligence.

https://www.ece.uw.edu/spotlight/miniaturized-imaging-device/
https://www.ece.uw.edu/spotlight/rosenthal-yang-research-award/
https://www.ece.uw.edu/spotlight/the-integrator-2021/
https://www.ece.uw.edu/spotlight/joshua-smith-nai/
https://www.ece.uw.edu/spotlight/uw-biofab-a-force-for-reproducible-science/
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                    [post_content] => By Wayne Gillam | UW ECE News

[caption id="attachment_24047" align="alignright" width="625"]Optical GAN illustration An illustration of the UW ECE-led research team’s integrated optical computing chip and “handwritten” numbers it generated. The chip contains an artificial neural network that can learn how to write like a human in its own, distinct style. This optical computing system uses “noise” (stray photons from lasers and thermal background radiation) to augment its creative capabilities. The system is also approximately 10 times faster than comparable conventional digital computers and more energy efficient, helping to put AI and machine learning on a path toward environmental sustainability. Illustration by Changming Wu[/caption]

Artificial intelligence and machine learning are currently affecting our lives in a myriad of small but impactful ways. For example, AI and machine learning applications help to interpret voice commands given to our phones and electronic devices, such as Alexa, and recommend entertainment we might enjoy through services such as Netflix and Spotify. In the near future, it’s predicted that AI and machine learning will have an even larger impact on society through activities such as driving fully autonomous vehicles, enabling complex scientific research and facilitating medical discoveries.

But the computers used for AI and machine learning demand energy, and lots of it. Currently, the need for computing power related to these technologies is doubling roughly every three to four months. And cloud computing data centers used by AI and machine learning applications worldwide are already devouring more electrical power per year than some small countries. Knowing this, it’s easy to see that this level of energy consumption is unsustainable, and if left unchecked, will come with serious environmental consequences for us all.

UW ECE Professor Mo Li and graduate student Changming Wu have been working toward addressing this daunting challenge over the last couple of years, developing new optical computing hardware for AI and machine learning that is faster and much more energy efficient than conventional electronics. They have already engineered an optical computing system that uses laser light to transmit information and do computing by using phase-change material similar to what is in a CD or DVD-ROM to record data. Laser light transmits data much faster than electrical signals, and phase-change material can retain data using little to no energy. With these advantages, their optical computing system has proven to be much more energy efficient and over 10 times faster than comparable digital computers.

[caption id="attachment_24048" align="alignright" width="400"]Mo Li and Changming Wu headshots UW ECE Professor Mo Li (left) and UW ECE graduate student Changming Wu (right) led the interdisciplinary, multi-institutional research team that built this optical computing system. Mo Li photo by Ryan Hoover[/caption]

Now, Li and Wu are addressing another key challenge, the ‘noise’ inherent to optical computing itself. This noise essentially comes from stray light particles, photons, that interfere with computing precision. These errant photons come from the operation of lasers within the device and background thermal radiation. In a new paper published on Jan. 21 in Science Advances, Li, Wu and their research team demonstrate a first-of-its-kind optical computing system for AI and machine learning that not only mitigates this noise but actually uses some of it as input to help enhance the creative output of the artificial neural network within the system. This work resulted from an interdisciplinary collaboration of Li’s research group at the UW with computer scientists Yiran Chen and Xiaoxuan Yang at Duke University and material scientists Ichiro Takeuchi and Heshan Yu at the University of Maryland.

“We’ve built an optical computer that is faster than a conventional digital computer,” said Wu, who is the paper’s lead author. “And also, this optical computer can create new things based on random inputs generated from the optical noise that most researchers tried to evade.”

Using noise to enhance AI creativity

Artificial neural networks are bedrock technology for AI and machine learning. These networks function in many respects like the human brain, taking in and processing information from various inputs and generating useful outputs. In short, they are capable of learning. In this research work, the team connected Li and Wu’s optical computing core to a special type of artificial neural network called a Generative Adversarial Network, or GAN, which has the capacity to creatively produce outputs. The team employed several different noise mitigation techniques, which included using some of the noise generated by the optical computing core to serve as random inputs for the GAN. The team found that this technique not only made the system more robust, but it also had the surprising effect of enhancing the network’s creativity, allowing it to generate outputs with more varying styles.
"This optical system represents a computer hardware architecture that can enhance the creativity of artificial neural networks used in AI and machine learning, but more importantly, it demonstrates the viability for this system at a large scale where noise and errors can be mitigated and even harnessed. AI applications are growing so fast that in the future, their energy consumption will be unsustainable. This technology has the potential to help reduce that energy consumption, making AI and machine learning environmentally sustainable and very fast, achieving higher performance overall." — UW ECE Professor Mo Li
To experimentally test the image creation abilities of their device, the team assigned the GAN the task of learning how to handwrite the number “7” like a human. The optical computer could not simply print out the number according to a prescribed font. It had to learn the task much like a child would, by looking at visual samples of handwriting and practicing until it could write the number correctly. Of course, the optical computer didn’t have a human hand for writing, so its form of “handwriting” was to generate digital images that had a style similar to the samples it had studied but were not identical to them. “Instead of training the network to read handwritten numbers, we trained the network to learn to write numbers, mimicking visual samples of handwriting that it was trained on,” Li said. “We, with the help of our computer science collaborators at Duke University, also showed that the GAN can mitigate the negative impact of the optical computing hardware noises by using a training algorithm that is robust to errors and noises. More than that, the network actually uses the noises as random input that is needed to generate output instances.” After learning from handwritten samples of the number seven, which were from a standard AI-training image set, the GAN practiced writing “7” until it could do it successfully. Along the way, it developed its own, distinct writing style. The team was also able to get the device to write numbers from one to 10 in computer simulations. As a result of this research, the team was able to show that an optical computing device could power a sophisticated form of artificial intelligence, and that the noise inherent to integrated optoelectronics was not a barrier, but in fact could be used to enhance AI creativity. They also showed that the technology in their device was scalable, and that it would be possible for it to be deployed widely, for instance, in cloud computing data centers worldwide. Next steps for the research team will be to build their device at a larger scale using current semiconductor manufacturing technology. So, instead of constructing the next iteration of the device in a lab, the team plans to use an industrial semiconductor foundry to achieve wafer-scale technology. A larger scale device will further improve performance and allow the research team to do more complex tasks beyond handwriting generation such as creating artwork and even videos. “This optical system represents a computer hardware architecture that can enhance the creativity of artificial neural networks used in AI and machine learning, but more importantly, it demonstrates the viability for this system at a large scale where noise and errors can be mitigated and even harnessed,” Li said. “AI applications are growing so fast that in the future, their energy consumption will be unsustainable. This technology has the potential to help reduce that energy consumption, making AI and machine learning environmentally sustainable — and very fast, achieving higher performance overall.” This research is financially supported by the Office of Naval Research and the National Science Foundation. For more information, contact Mo Li. [post_title] => Harnessing noise in optical computing for AI [post_excerpt] => [post_status] => publish [comment_status] => closed [ping_status] => closed [post_password] => [post_name] => using-noise-optical-computing-for-ai [to_ping] => [pinged] => [post_modified] => 2022-01-21 17:26:53 [post_modified_gmt] => 2022-01-22 01:26:53 [post_content_filtered] => [post_parent] => 0 [guid] => https://www.ece.uw.edu/?post_type=spotlight&p=24042 [menu_order] => 1 [post_type] => spotlight [post_mime_type] => [comment_count] => 0 [filter] => raw ) [1] => WP_Post Object ( [ID] => 24012 [post_author] => 25 [post_date] => 2022-01-18 12:12:13 [post_date_gmt] => 2022-01-18 20:12:13 [post_content] => Article by Renske Dyedov |  UW Institute for Nano-Engineered Systems (NanoES) Cardiovascular diseases (CVDs) are the leading cause of death in the U.S. and around the world. According to the World Health Organization, an estimated 17.9 million people die from CVDs each year. The vast majority of these deaths are due to heart attacks and strokes. [caption id="attachment_24014" align="alignright" width="320"] Arka Majumdar. Photo by Ryan Hoover[/caption] Heart attacks and strokes happen when a buildup of plaque, or fatty deposits, on the inner walls of arteries prevents blood from flowing to the heart or brain. To remove these blockages, surgeons need a real-time, high-resolution view into the artery. But conventional optical elements currently used in endoscopes are too bulky and stiff to reach diseased arteries deep in the heart or brain. Recently, an interdisciplinary research team at the University of Washington (UW), led by Arka Majumdar, an associate professor of electrical and computer engineering and physics, was awarded $3.6 million in funding from the National Science Foundation (NSF) to take on this challenge. Using the emerging nanophotonics and metamaterial technology meta-optics in combination with advanced computational post-processing, the team aims to develop a dramatically smaller endoscope to image previously inaccessible areas of the heart and brain. Unlike the thick glass lenses of traditional cameras, meta-optics are nearly two-dimensional, studded with over a million tiny cylindrical structures that capture and re-emit light at the nanometer scale. The unique geometry and arrangement of these nanostructures determines how these extremely thin optical elements, less than one millimeter thick, interact with light to produce an image. “Others have attempted to incorporate meta-optics in endoscopes, but never before has machine learning been used to both design the meta-optics and improve image quality on the backend,” said Majumdar. “On their own, meta-optics suffer from poor resolution and color aberrations. But by merging meta-optics with machine learning, we will make a miniaturized imaging system that can produce high-resolution, full-color images with an extended field of view.” [caption id="attachment_24013" align="alignleft" width="441"] Co-investigators Eric Seibel (left) and Karl Böhringer (right)[/caption] To build a miniaturized scope that can be used by surgeons, Majumdar is bringing together a highly diverse team that includes academic researchers, medical professionals and startups all based in Washington State. Majumdar’s co-investigators include nanofabrication expert Karl Böhringer, a professor of electrical & computer engineering and bioengineering at the UW, and Eric Seibel, a professor of mechanical engineering at the UW and expert in ultrathin and flexible medical imaging devices. Machine learning expert Steve Brunton, also a professor of mechanical engineering at the UW, will play a key role in optimizing the meta-optics design. The research team will work with neurovascular and cardiovascular surgeons Drs. Luis Savastano and John Petersen to better understand the needs of their end user. They will also collaborate with UW spinouts Tunoptix, cofounded by Majumdar and Böhringer, and VerAvanti, which is commercializing a technology pioneered by Seibel known as Scanning Fiber Endoscopy (SFE), to figure out how to manufacture and commercialize these scopes at scale. “Current scopes utilize 50-year-old technology consisting of bundles of glass optical fibers that form a rigid tip, limiting where the scopes can go,” said Seibel. “SFE uses lasers and a micro-electro-mechanical scanner to reduce the length of this rigid tip from nine to about five millimeters. Meta-optics will reduce this even further to two and a half millimeters or less, giving surgeons full access to areas of the body that currently cannot be visualized at all.” [caption id="attachment_24015" align="alignright" width="575"] Spectrally Encoded Non-Scanning Endoscopy (SENSE): (a) The current state-of-the art scanning fiber endoscopes can be significantly miniaturized to create a (b) spectrally encoded non-scanning endoscope. (c) The key will be spatial-spectral mapping using dispersive meta-optics, which can be computationally decoded to recover the images.[/caption] Key to the viability of this device in the real world is that it is both inexpensive to produce and disposable. Meta-optics are fabricated using the same semiconductor manufacturing techniques that produce computer chips. “We are fortunate to have access to the largest publicly accessible nanofabrication facility in the Pacific Northwest right here on the UW campus,” said Böhringer, who as the director for the Institute for Nano-engineered Systems oversees the Washington Nanofabrication Facility (WNF), an open-access user facility that provides academic researchers and industry professionals access to nanofabrication tools and expertise. “Microfabrication has successfully been used to mass produce semiconductors, drastically reducing their cost. Certainly, there is a lot of evidence that meta-optics can similarly be made at scale, but it remains an open question exactly how to do this.” The team will begin by working to demonstrate that meta-optics can be used to reduce the tip length of both conventional endoscopes and scanning fiber endoscopes. They also hope to establish the feasibility of their longer-term goal of integrating computational processing and meta-optics to achieve the smallest and most agile endoscope possible. The team’s clinical and industry collaborators will provide input to help ensure a useful and feasible end product. Eventually, clinicians working at the frontiers of endovascular surgery will help test the device. “There are lots of potential applications for meta-optics – everything from cell phone cameras to satellites – but I think that its integration in biomedical devices such as endoscopes is where meta-optics will have the biggest impact,” said Majumdar. “The need is clear. Miniaturization of the optical elements in endoscopes could transform the treatment of cardiovascular disease and ultimately save lives.” [post_title] => UW researchers developing miniaturized imaging device to treat heart attack, stroke [post_excerpt] => [post_status] => publish [comment_status] => closed [ping_status] => closed [post_password] => [post_name] => miniaturized-imaging-device [to_ping] => [pinged] => [post_modified] => 2022-01-18 14:41:26 [post_modified_gmt] => 2022-01-18 22:41:26 [post_content_filtered] => [post_parent] => 0 [guid] => https://www.ece.uw.edu/?post_type=spotlight&p=24012 [menu_order] => 2 [post_type] => spotlight [post_mime_type] => [comment_count] => 0 [filter] => raw ) [2] => WP_Post Object ( [ID] => 23902 [post_author] => 27 [post_date] => 2022-01-10 17:04:08 [post_date_gmt] => 2022-01-11 01:04:08 [post_content] => By Wayne Gillam | UW ECE News [caption id="attachment_23912" align="alignright" width="600"]James Rosenthal holding the NeuroDisc prototype Yang Research Award recipient James Rosenthal holds a NeuroDisc prototype, which he developed at UW ECE in the lab of Professor Matt Reynolds. The NeuroDisc is a low-power wireless brain-computer interface intended for use in electrophysiology experiments. Photo courtesy of Matt Reynolds[/caption] UW ECE congratulates recent graduate James Rosenthal, who was named as the 2021 Yang Research Award recipient for his work developing neurotechnology. Rosenthal received his master’s and doctoral degrees from UW ECE in 2018 and 2021, respectively. He is currently a Marie Curie Postdoctoral Fellow with the Laboratory for Soft Bioelectronic Interfaces at the Ecole Polytechnique Fédérale de Lausanne in Geneva, Switzerland. Rosenthal completed his doctoral degree at UW ECE as a National Science Graduate Research Fellow, and he was advised by UW ECE Associate Professor Matt Reynolds. Rosenthal’s research focuses on the development of wireless brain-computer interfaces. He explores how architectural innovation in embedded systems can reduce the size, weight and power consumption of wireless systems, which in turn can enable new methods for monitoring and treating neurological disorders. “I am extremely honored to be this year’s recipient,” Rosenthal stated in his acknowledgement of the award. “There are many uncertainties throughout a doctoral program, and it is often easy to question the impact of one’s research. To be selected for this award by leading faculty from our department brings me a lot of confidence as I seek to continue my career in research as a professor.”
The purpose of the Yang Research Award is to recognize and encourage outstanding doctoral student research contributions to the field of electrical engineering. The award goes to one qualifying student per year and is open to all doctoral degree candidates in UW ECE. Receiving the award is considered a high honor and helps to create career opportunities for the recipient.
Rosenthal is passionate about teaching, outreach and mentoring. He was the instructor of record for EE417–Wireless Communications in 2020 and 2021, and he was a teaching assistant for over six academic quarters at UW ECE. He served as UW ECE Graduate Student Association co-chair from 2017 to 2018, and he also served as one of two UW ECE graduate and professional student senators for the University from 2018 to 2020. Rosenthal also co-hosted a scientific communication podcast, “The Paperboys,” with fellow UW graduate student Charlie Kelly. Each episode, Rosenthal and Kelly read and discussed the research papers behind headline science news. “James is one of the very top Ph.D. students to have graduated from UW ECE in the last several years,” Reynolds said. “In addition to his many strengths in research, he was an outstanding mentor to the other students working in my lab, and he is also an excellent teacher.” The Yang Research Award was established by successful entrepreneur and former UW ECE faculty member Andrew T. Yang, who spoke at the UW ECE Graduation Celebration in 2012. Yang has been one of the most influential people in the electronic design automation industry for nearly three decades, and he is known for being a visionary in both research and entrepreneurship. When creating the award, Yang stated that he received a similar award when he was a doctoral student and that the recognition gave him the confidence and motivation he needed to continue his promising career in electrical engineering research. Rosenthal has been engaged in groundbreaking research at the doctoral and postdoctoral stages of his academic career. In addition to expressing gratitude for the award, he noted the supportive environment he found at the University of Washington. “The University of Washington offered a unique, interdisciplinary environment to explore my research that would have been difficult to find anywhere else,” Rosenthal stated. “The emphasis on collaborative research, particularly through the Center for Neurotechnology, offered me the opportunity to work alongside leaders in wireless electronics, neuroscience and translational medicine.” [post_title] => James Rosenthal receives Yang Research Award [post_excerpt] => [post_status] => publish [comment_status] => closed [ping_status] => closed [post_password] => [post_name] => rosenthal-yang-research-award [to_ping] => [pinged] => [post_modified] => 2022-01-10 17:04:08 [post_modified_gmt] => 2022-01-11 01:04:08 [post_content_filtered] => [post_parent] => 0 [guid] => https://www.ece.uw.edu/?post_type=spotlight&p=23902 [menu_order] => 3 [post_type] => spotlight [post_mime_type] => [comment_count] => 0 [filter] => raw ) [3] => WP_Post Object ( [ID] => 23833 [post_author] => 26 [post_date] => 2021-12-20 09:23:10 [post_date_gmt] => 2021-12-20 17:23:10 [post_content] =>  

Dear UW ECE Community,

I am excited to introduce the 2021 issue of The Integrator — UW ECE's flagship, annual magazine highlighting the Department's extraordinary faculty research, student achievements, alumni stories, special events and more. Beginning this year, we have transitioned The Integrator into a fully-digital, environmentally sustainable online publication model, made available to our readers as an easily accessible, interactive experience. We hope you enjoy the new format! This has been another extraordinary year for UW ECE. Our faculty, students and alumni continue to do amazing work even in the face of continued challenges to universities and the world-at-large due to the ongoing pandemic. It’s abundantly clear that UW ECE has continued to thrive with its tremendously vibrant and supportive community of individuals coming together to make important, world-changing engineering advances. It is my sincere honor to serve as chair of this outstanding department. I hope you enjoy this year's issue of The Integrator and take some time to read about our phenomenal UW ECE community and its activities. As always, feel free to reach out to us anytime — we’d love to hear from you!

Read The Integrator 2021 here!

  Eric Klavins Eric Klavins Professor and Chair, UW Department of Electrical & Computer Engineering
Check out past issues of The Integrator here. [post_title] => The Integrator 2021 - Focus on Impact - now available! [post_excerpt] => [post_status] => publish [comment_status] => closed [ping_status] => closed [post_password] => [post_name] => the-integrator-2021 [to_ping] => [pinged] => [post_modified] => 2022-01-05 12:47:52 [post_modified_gmt] => 2022-01-05 20:47:52 [post_content_filtered] => [post_parent] => 0 [guid] => https://www.ece.uw.edu/?post_type=spotlight&p=23833 [menu_order] => 6 [post_type] => spotlight [post_mime_type] => [comment_count] => 0 [filter] => raw ) [4] => WP_Post Object ( [ID] => 23771 [post_author] => 25 [post_date] => 2021-12-09 13:43:28 [post_date_gmt] => 2021-12-09 21:43:28 [post_content] => [caption id="attachment_23815" align="aligncenter" width="1024"]Professor Joshua R. Smith was elected into the 2021 class of Fellows of the National Academy of Inventors for his impactful creations in the fields of wireless power, communication, sensing and robotics. Josh Smith, the Milton and Delia Zeutschel Professor in Entrepreneurial Excellence, was elected into the 2021 class of Fellows of the National Academy of Inventors for his impactful creations in the fields of wireless power, communication, sensing and robotics.[/caption] Adapted from article by Rebekka Coakley |  Paul G. Allen School of Computer Science & Engineering Professor Joshua R. Smith, the Milton and Delia Zeutschel Professor in Entrepreneurial Excellence, who holds a joint appointment in the Department of Electrical & Computer Engineering (UW ECE) and the Paul G. Allen School of Computer Science & Engineering, was elected into the 2021 class of Fellows of the National Academy of Inventors (NAI) for his impactful creations in the fields of wireless power, communication, sensing and robotics. Smith, who leads the Sensor Systems Lab, is one of only five University of Washington faculty members to have received this prestigious award that highlights the prolific spirit of innovation in academic inventors. The NAI Fellows program was created to recognize inventors and their contributions to society, which stimulate the economy, improve and save lives, and make the world a better place. It is the highest professional distinction given solely to academic inventors. "Josh has inspired all of us with his leadership in entrepreneurship and innovation," said UW ECE Professor and Chair Eric Klavins. "This is part of why our Department continues to be the number one generator of startup companies at the University. He also models for our students how to create and develop innovative technology that has far-reaching, positive impacts on society. He simply embodies the best of the best." [caption id="attachment_23810" align="alignleft" width="386"]Smith as a graduate student at MIT using mutual capacitance measurements to track hands in the air. Smith as a graduate student at MIT using mutual capacitance measurements to track hands in the air.[/caption] The NAI Fellow selection process considers inventions that have been licensed or commercialized. Smith holds 48 U.S. patents and 16 international patents, 44 of which are licensed by companies. His inventions have led to hundreds of millions of dollars in product revenues, bolstering the economy and the creation of approximately 70 full-time jobs, according to Suzie Pun, a professor in the Department of Bioengineering and another UW faculty member who is an NAI Fellow. His first six patents, developed while a graduate student at MIT, pioneered mutual capacitance sensing and led to the creation of a smart airbag system that was included in every Honda car between 2000 and 2015. Before his arrival at UW, Smith spent five years at Intel Research Seattle, creating new capabilities in wireless power, wireless sensing and robotics. He led the creation of the Wireless Identification and Sensing Platform (WISP), the first fully programmable platform for wireless, battery-free sensing and computation powered by radio waves. Soon after, he developed Wireless Resonant Energy Link (WREL), which uses magnetically coupled resonators to efficiently transfer wireless power even as range, orientation and load vary. With the help of a heart surgeon from Yale, Smith was able to power a ventricular assist device designed for implantation in the human body without requiring a cable through the patient’s chest, called the Free-range Resonant Electrical Energy Delivery System (FREED). This wireless power work at UW is commercialized by WiBotic, a company Smith co-founded with ECE alumnus Benjamin Waters (Ph.D., ‘15). The UW patents are also licensed for implanted heart pumps by Corisma. [caption id="attachment_23805" align="aligncenter" width="1024"]Ambient backscatter devices powered by radio waves from a TV tower in the background. They communicate with one another by selectively reflecting the tower’s radio signals. Ambient backscatter devices powered by radio waves from a TV tower in the background. They communicate with one another by selectively reflecting the tower’s radio signals.[/caption] “Among the many outstandingly inventive engineers at Intel Research Seattle, we were especially excited that Josh joined our faculty, he is extraordinary in every imaginable respect,” said Ed Lazowska, professor and Bill & Melinda Gates Chair Emeritus at the Allen School. “He is an academic inventor and entrepreneur of the highest caliber and in the finest tradition.”
"Invention is just one part of a long process to bring new things into the world. I am very grateful to the many people who have worked so hard to take these inventions from the lab to the world." - Josh Smith
In 2013, Smith, together with Allen School professor Shyam Gollakota and a team of graduate students, developed Ambient Backscatter using existing wireless signals to provide power and communication for low-power sensing and computing devices. This next led to the creation of Passive-Wi-Fi, bringing low-power Wi-Fi to transmissions. They also invented Interscatter, using wireless transmissions over the air from one technology to another for internet-connected implanted devices. Smith also co-led the UW team behind the world’s first battery‐free phone, as well as a series of ultra-low-power battery-free wireless cameras that communicate via backscatter. This research is being commercialized by Jeeva Wireless, a UW spinout co-founded by Smith, Gollakota, and ECE alumni Vamsi Talla (Ph.D., ‘16) and Aaron Parks (Ph.D., ‘17). [caption id="attachment_23776" align="alignleft" width="434"]A robot using non-contact pre-touch sensing to solve the Rubik’s Cube. A robot using non-contact pre-touch sensing to solve the Rubik’s Cube.[/caption] “Josh has a consistent record of impactful inventions,” said Pun. “I have gotten to know him through a research collaboration to develop touchscreen-based sensors for detection of pathogens such as SARS-CoV-2. Josh devised a creative method to improve detection sensitivity for the virus; he is in the process of testing this idea in his laboratory. If successful, his design could be applied for next-generation biosensing devices.” Smith also co-founded Proprio, which provides surgical visualization and navigation, together with UW neurosurgeon Sam Browd, Allen School graduate student Jim Youngquist, UW Foundation board member Ken Denman, and Michael G. Foster School of Business alumnus Gabe Jones (MBA, ‘14). Smith served on advisory councils and task forces for the United States Postal Service and the Smithsonian Institution and is an IEEE Fellow. His work has earned multiple Best Paper Awards, and he is known for his dedicated mentorship of student researchers. “I feel so privileged to collaborate with my outstanding UW faculty and student co-inventors,” said Smith. “And invention is just one part of a long process to bring new things into the world. I am very grateful to the many people who have worked so hard to take these inventions from the lab to the world, including UW CoMotion, many patent attorneys, and most of all the co-founders and employees at the companies making these technologies real.” [caption id="attachment_23801" align="alignright" width="372"] A robot using acoustic levitation to hold a flower without touching it.[/caption] [caption id="attachment_23800" align="alignleft" width="675"] Wireless Ambient Radio Power: A kitchen thermometer is powered by radio waves from the tower in the background.[/caption]
Read the NAI announcement here, and the full list of 2021 Fellows here. Congratulations, Josh! [post_title] => Professor Joshua R. Smith elected Fellow of the National Academy of Inventors for his innovations in wireless power, communication, sensing and robotics [post_excerpt] => [post_status] => publish [comment_status] => closed [ping_status] => closed [post_password] => [post_name] => joshua-smith-nai [to_ping] => [pinged] => [post_modified] => 2021-12-14 09:04:38 [post_modified_gmt] => 2021-12-14 17:04:38 [post_content_filtered] => [post_parent] => 0 [guid] => https://www.ece.uw.edu/?post_type=spotlight&p=23771 [menu_order] => 7 [post_type] => spotlight [post_mime_type] => [comment_count] => 0 [filter] => raw ) [5] => WP_Post Object ( [ID] => 23652 [post_author] => 26 [post_date] => 2021-11-24 10:21:02 [post_date_gmt] => 2021-11-24 18:21:02 [post_content] =>

UW BIOFAB: A force for reproducible science

The UW’s Biofabrication Center, founded by UW ECE Professor and Chair Eric Klavins, partners with Agilent Technologies in pursuit of automated, reproducible research. [caption id="attachment_23658" align="aligncenter" width="907"]Undergraduate technicians perform common molecular biology tasks to enable the rapid design, construction and testing of genetically reprogrammed organisms for biotechnology applications and research. Dennis Wise / University of Washington Undergraduate technicians perform common molecular biology tasks to enable the rapid design, construction and testing of genetically reprogrammed organisms for biotechnology applications and research. Dennis Wise / University of Washington[/caption]   Article by Renske Dyedov, UW Institute for Nano-Engineered Systems (NanoES)

Key to advancing any new scientific discovery is the ability for researchers to independently repeat the experiments that led to it. In science today, particularly biology, the lack of reproducibility between experiments is a major problem that slows scientific progress, wastes resources and time, and erodes the public’s trust in scientific research.

[caption id="attachment_23672" align="alignright" width="232"]UW ECE Professor and Chair, Eric Klavins. Dennis Wise / University of Washington BIOFAB founder, and UW ECE Professor and Chair, Eric Klavins. Dennis Wise / University of Washington[/caption] At the University of Washington, researchers have access to the UW Biofabrication Center, or BIOFAB, a unique facility located in the Nanoengineering and Sciences building in which scientific protocols are encoded as computer programs, allowing undergraduate lab technicians to execute experiments according to detailed instructions. “The BIOFAB is unlike any other lab on campus,” says BIOFAB founder Eric Klavins, Professor and Chair of the UW Electrical & Computer Engineering Department (UW ECE). “In effect, we’ve been able to automate common protocols by using software to assist our student technicians. This ‘human-in-the-loop’ system goes a long way towards improving the replicability of biological research.” In an effort to expand the lab’s automation capabilities, the BIOFAB has partnered with Agilent Technologies Inc., a life sciences development and manufacturing company based in California’s Silicon Valley. Using state-of-the-art research equipment from Agilent, the BIOFAB will develop high-throughput workflows for common tasks of interest to members of the synthetic biology community.

Programming the biology lab

Computer programmers write code to tell a computer what to do and how to do it. For a given program, the same inputs consistently result in the same outputs. In contrast, two biology researchers can seemingly carry out the same experiment, but get different results. This is in part because instructions for how the experiment was conducted – whether documented in a lab notebook or published in a journal – are often vague or incomplete, leaving out details that the author may not have realized impacted the experimental outcome. [caption id="attachment_23657" align="alignleft" width="580"]Aquarium is a web-based software application that allows scientists to build executable protocols, design experimental workflows based on those protocols, manage the execution of protocols in the lab and automatically record the resulting data. Dennis Wise / University of Washington Aquarium is a web-based software application that allows scientists to build executable protocols, design experimental workflows based on those protocols, manage the execution of protocols in the lab and automatically record the resulting data. Dennis Wise / University of Washington[/caption] As a computer scientist turned synthetic biologist, Klavins realized what biologists needed was a more formal way – a programming language – to define how to conduct an experiment. This led to the development of Aquarium, a web-based software application that allows scientists to build executable protocols, design experimental workflows based on those protocols, manage the execution of protocols in the lab and automatically record the resulting data. “Aquarium provides the means to specify, as precisely as possible, how to obtain a result,” said Klavins. When it comes to engineering biology – reprogramming cells to produce chemicals or drugs, or perform complex functions like sensing toxic compounds in the environment – reproducibility is paramount. The BIOFAB uses Aquarium to standardize various scientific workflows, generating reliable and highly reproducible results. The BIOFAB is one of a growing number of labs known as biofoundries which are committed to efficiently engineering biological systems and workflows. BIOFAB operations are overseen by two lab managers, with a dozen or so undergraduate students executing jobs for BIOFAB clients. BIOFAB technicians perform common molecular biology tasks like DNA assembly and purification as a fee-for-service to the scientific community. Since its founding in 2014, the BIOFAB has run over 30,000 jobs for 300+ different clients at the UW and beyond. “The BIOFAB has been absolutely instrumental in establishing and executing robust Aquarium driven protocols for a major portion of our de novo design minibinder pipeline,” said Lance Stewart, Chief Strategy and Operations Officer at the UW’s Institute for Protein Design (IPD). IPD researchers use computers to design millions of minibinders – small, stable proteins that bind with high affinity to targets of interest – that must be produced and tested in the lab. IPD uses the BIOFAB to screen minibinder candidates for protein stability and protein:protein interactions, which involves constructing yeast libraries from chip synthesized oligonucleotide genes encoding minibinder designs and carrying out large scale fluorescence activated cell sorting and next generation DNA sequencing. “By handing off time-consuming wet lab work to our technicians, BIOFAB clients like IPD can focus more on the design and data analysis aspects of their experiments,” said Klavins.

Learning by doing

[caption id="attachment_23659" align="alignright" width="330"]BIOFAB technician Nicole Roullier. Dennis Wise / University of Washington BIOFAB technician Nicole Roullier. Dennis Wise / University of Washington[/caption] On any given day, the BIOFAB is buzzing with undergraduate technicians working together in harmony to complete an assortment of experiments for BIOFAB clients. Most technicians start working in the BIOFAB as freshman or sophomores, and for many, it’s their first real lab experience. Upon joining the lab, BIOFAB lab managers teach students basic lab skills, such as pipetting and sterile technique, and orient them to the lab. Armed with this foundational knowledge, BIOFAB technicians can begin executing a variety of different protocols by following the step-by-step instructions provided through Aquarium. Students become adept at performing complicated experimental workflows involving complex equipment through the process of doing them over and over again. “Aquarium allows us to effectively train many students simultaneously and get them working in the lab relatively quickly,” said Aza Allen, a lab manager at the BIOFAB. “Aquarium’s technician interface makes it easy to get undergraduate students, who do not necessarily know much about molecular biology when they start, to perform experiments reliably.” “I have learned so much beyond what could possibly be taught in a classroom setting,” said BIOFAB technician Nicole Roullier, a UW biochemistry senior. “Most undergraduates don’t have the opportunity to work with such sophisticated equipment and master advanced techniques like qPCR and next-generation sequencing (NGS). This hands-on training has built up my confidence in the lab in preparation for graduate school.”

A promising partnership

The BIOFAB provides critical automation and analytics infrastructure dedicated to enabling the rapid design, construction and testing of genetically reprogrammed organisms for biotechnology applications and research. Through its partnership with Agilent, the BIOFAB aims to offer new high-throughput capabilities that will further speed up and scale up synthetic biology research. “We’re thrilled to be partnering with Agilent,” said Klavins. “Their support will not only accelerate the development of innovative technologies, but will help us educate students using cutting-edge equipment, bolstering our ability to prepare students for success in their own future research and career.” “We think this is the start of an exciting collaboration,” said Kevin Meldrum, General Manager and Vice President of Genomics at Agilent. “We are pleased to be able to support researchers at the UW and the educational mission of the university through the BIOFAB. We see this as an investment in the future of our field.” [caption id="attachment_23660" align="alignleft" width="630"]Agilent’s state-of-the-art liquid-handling robot, the Bravo Automated Liquid Handling platform will help speed up and scale up synthetic biology research. Dennis Wise / University of Washington Agilent’s state-of-the-art liquid-handling robot, the Bravo Automated Liquid Handling platform will help speed up and scale up synthetic biology research. Dennis Wise / University of Washington[/caption] As a result of this partnership, the BIOFAB has acquired several valuable pieces of equipment, including Agilent’s state-of-the-art liquid-handling robot, the Bravo Automated Liquid Handling platform. While the Bravo can be used to automate sample preparation for a variety of different applications, the BIOFAB plans to initially use it to expedite its workflow for NGS. In addition to the Bravo, the BIOFAB has also acquired the AriaMx Real-Time PCR System, and the 5200 Fragment Analyzer System, a parallel capillary electrophoresis system. “Library preparation for high-throughput NGS is a tedious, labor-intensive process,” said Klavins. “Agilent’s Bravo will help make this workflow more efficient and reduce pipetting errors that make results less consistent, while also freeing up time for our technicians to work on less repetitive tasks. We know that there are certainly other workflows that would benefit from the use of Bravo, and we plan to engage BIOFAB users to identify which ones to pursue. We are thrilled to be able to bring this resource to the UW community, and are excited to see the compelling science that comes out as a result.” [post_title] => UW BIOFAB: A force for reproducible science [post_excerpt] => [post_status] => publish [comment_status] => closed [ping_status] => closed [post_password] => [post_name] => uw-biofab-a-force-for-reproducible-science [to_ping] => [pinged] => [post_modified] => 2021-12-07 12:39:27 [post_modified_gmt] => 2021-12-07 20:39:27 [post_content_filtered] => [post_parent] => 0 [guid] => https://www.ece.uw.edu/?post_type=spotlight&p=23652 [menu_order] => 8 [post_type] => spotlight [post_mime_type] => [comment_count] => 0 [filter] => raw ) ) [_numposts:protected] => 6 [_rendered:protected] => 1 [_classes:protected] => Array ( [0] => view-block [1] => block--spotlight-robust-news ) [_finalHTML:protected] =>
https://www.ece.uw.edu/spotlight/using-noise-optical-computing-for-ai/
Harnessing noise in optical computing for AI

Harnessing noise in optical computing for AI

A multi-institutional, interdisciplinary research team led by UW ECE Professor Mo Li has found innovative ways of using noise inherent to integrated optoelectronics to enhance the creativity of artificial intelligence.

https://www.ece.uw.edu/spotlight/miniaturized-imaging-device/
https://www.ece.uw.edu/spotlight/rosenthal-yang-research-award/
https://www.ece.uw.edu/spotlight/the-integrator-2021/
https://www.ece.uw.edu/spotlight/joshua-smith-nai/
https://www.ece.uw.edu/spotlight/uw-biofab-a-force-for-reproducible-science/
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The chip contains an artificial neural network that can learn how to write like a human in its own, distinct style. This optical computing system uses “noise” (stray photons from lasers and thermal background radiation) to augment its creative capabilities. The system is also approximately 10 times faster than comparable conventional digital computers and more energy efficient, helping to put AI and machine learning on a path toward environmental sustainability. Illustration by Changming Wu[/caption] Artificial intelligence and machine learning are currently affecting our lives in a myriad of small but impactful ways. For example, AI and machine learning applications help to interpret voice commands given to our phones and electronic devices, such as Alexa, and recommend entertainment we might enjoy through services such as Netflix and Spotify. In the near future, it’s predicted that AI and machine learning will have an even larger impact on society through activities such as driving fully autonomous vehicles, enabling complex scientific research and facilitating medical discoveries. But the computers used for AI and machine learning demand energy, and lots of it. Currently, the need for computing power related to these technologies is doubling roughly every three to four months. And cloud computing data centers used by AI and machine learning applications worldwide are already devouring more electrical power per year than some small countries. Knowing this, it’s easy to see that this level of energy consumption is unsustainable, and if left unchecked, will come with serious environmental consequences for us all. UW ECE Professor Mo Li and graduate student Changming Wu have been working toward addressing this daunting challenge over the last couple of years, developing new optical computing hardware for AI and machine learning that is faster and much more energy efficient than conventional electronics. They have already engineered an optical computing system that uses laser light to transmit information and do computing by using phase-change material similar to what is in a CD or DVD-ROM to record data. Laser light transmits data much faster than electrical signals, and phase-change material can retain data using little to no energy. With these advantages, their optical computing system has proven to be much more energy efficient and over 10 times faster than comparable digital computers. [caption id="attachment_24048" align="alignright" width="400"]Mo Li and Changming Wu headshots UW ECE Professor Mo Li (left) and UW ECE graduate student Changming Wu (right) led the interdisciplinary, multi-institutional research team that built this optical computing system. Mo Li photo by Ryan Hoover[/caption] Now, Li and Wu are addressing another key challenge, the ‘noise’ inherent to optical computing itself. This noise essentially comes from stray light particles, photons, that interfere with computing precision. These errant photons come from the operation of lasers within the device and background thermal radiation. In a new paper published on Jan. 21 in Science Advances, Li, Wu and their research team demonstrate a first-of-its-kind optical computing system for AI and machine learning that not only mitigates this noise but actually uses some of it as input to help enhance the creative output of the artificial neural network within the system. This work resulted from an interdisciplinary collaboration of Li’s research group at the UW with computer scientists Yiran Chen and Xiaoxuan Yang at Duke University and material scientists Ichiro Takeuchi and Heshan Yu at the University of Maryland. “We’ve built an optical computer that is faster than a conventional digital computer,” said Wu, who is the paper’s lead author. “And also, this optical computer can create new things based on random inputs generated from the optical noise that most researchers tried to evade.”

Using noise to enhance AI creativity

Artificial neural networks are bedrock technology for AI and machine learning. These networks function in many respects like the human brain, taking in and processing information from various inputs and generating useful outputs. In short, they are capable of learning. In this research work, the team connected Li and Wu’s optical computing core to a special type of artificial neural network called a Generative Adversarial Network, or GAN, which has the capacity to creatively produce outputs. The team employed several different noise mitigation techniques, which included using some of the noise generated by the optical computing core to serve as random inputs for the GAN. The team found that this technique not only made the system more robust, but it also had the surprising effect of enhancing the network’s creativity, allowing it to generate outputs with more varying styles.
"This optical system represents a computer hardware architecture that can enhance the creativity of artificial neural networks used in AI and machine learning, but more importantly, it demonstrates the viability for this system at a large scale where noise and errors can be mitigated and even harnessed. AI applications are growing so fast that in the future, their energy consumption will be unsustainable. This technology has the potential to help reduce that energy consumption, making AI and machine learning environmentally sustainable and very fast, achieving higher performance overall." — UW ECE Professor Mo Li
To experimentally test the image creation abilities of their device, the team assigned the GAN the task of learning how to handwrite the number “7” like a human. The optical computer could not simply print out the number according to a prescribed font. It had to learn the task much like a child would, by looking at visual samples of handwriting and practicing until it could write the number correctly. Of course, the optical computer didn’t have a human hand for writing, so its form of “handwriting” was to generate digital images that had a style similar to the samples it had studied but were not identical to them. “Instead of training the network to read handwritten numbers, we trained the network to learn to write numbers, mimicking visual samples of handwriting that it was trained on,” Li said. “We, with the help of our computer science collaborators at Duke University, also showed that the GAN can mitigate the negative impact of the optical computing hardware noises by using a training algorithm that is robust to errors and noises. More than that, the network actually uses the noises as random input that is needed to generate output instances.” After learning from handwritten samples of the number seven, which were from a standard AI-training image set, the GAN practiced writing “7” until it could do it successfully. Along the way, it developed its own, distinct writing style. The team was also able to get the device to write numbers from one to 10 in computer simulations. As a result of this research, the team was able to show that an optical computing device could power a sophisticated form of artificial intelligence, and that the noise inherent to integrated optoelectronics was not a barrier, but in fact could be used to enhance AI creativity. They also showed that the technology in their device was scalable, and that it would be possible for it to be deployed widely, for instance, in cloud computing data centers worldwide. Next steps for the research team will be to build their device at a larger scale using current semiconductor manufacturing technology. So, instead of constructing the next iteration of the device in a lab, the team plans to use an industrial semiconductor foundry to achieve wafer-scale technology. A larger scale device will further improve performance and allow the research team to do more complex tasks beyond handwriting generation such as creating artwork and even videos. “This optical system represents a computer hardware architecture that can enhance the creativity of artificial neural networks used in AI and machine learning, but more importantly, it demonstrates the viability for this system at a large scale where noise and errors can be mitigated and even harnessed,” Li said. “AI applications are growing so fast that in the future, their energy consumption will be unsustainable. This technology has the potential to help reduce that energy consumption, making AI and machine learning environmentally sustainable — and very fast, achieving higher performance overall.” This research is financially supported by the Office of Naval Research and the National Science Foundation. For more information, contact Mo Li. [post_title] => Harnessing noise in optical computing for AI [post_excerpt] => [post_status] => publish [comment_status] => closed [ping_status] => closed [post_password] => [post_name] => using-noise-optical-computing-for-ai [to_ping] => [pinged] => [post_modified] => 2022-01-21 17:26:53 [post_modified_gmt] => 2022-01-22 01:26:53 [post_content_filtered] => [post_parent] => 0 [guid] => https://www.ece.uw.edu/?post_type=spotlight&p=24042 [menu_order] => 1 [post_type] => spotlight [post_mime_type] => [comment_count] => 0 [filter] => raw ) [1] => WP_Post Object ( [ID] => 24012 [post_author] => 25 [post_date] => 2022-01-18 12:12:13 [post_date_gmt] => 2022-01-18 20:12:13 [post_content] => Article by Renske Dyedov |  UW Institute for Nano-Engineered Systems (NanoES) Cardiovascular diseases (CVDs) are the leading cause of death in the U.S. and around the world. According to the World Health Organization, an estimated 17.9 million people die from CVDs each year. The vast majority of these deaths are due to heart attacks and strokes. [caption id="attachment_24014" align="alignright" width="320"] Arka Majumdar. Photo by Ryan Hoover[/caption] Heart attacks and strokes happen when a buildup of plaque, or fatty deposits, on the inner walls of arteries prevents blood from flowing to the heart or brain. To remove these blockages, surgeons need a real-time, high-resolution view into the artery. But conventional optical elements currently used in endoscopes are too bulky and stiff to reach diseased arteries deep in the heart or brain. Recently, an interdisciplinary research team at the University of Washington (UW), led by Arka Majumdar, an associate professor of electrical and computer engineering and physics, was awarded $3.6 million in funding from the National Science Foundation (NSF) to take on this challenge. Using the emerging nanophotonics and metamaterial technology meta-optics in combination with advanced computational post-processing, the team aims to develop a dramatically smaller endoscope to image previously inaccessible areas of the heart and brain. Unlike the thick glass lenses of traditional cameras, meta-optics are nearly two-dimensional, studded with over a million tiny cylindrical structures that capture and re-emit light at the nanometer scale. The unique geometry and arrangement of these nanostructures determines how these extremely thin optical elements, less than one millimeter thick, interact with light to produce an image. “Others have attempted to incorporate meta-optics in endoscopes, but never before has machine learning been used to both design the meta-optics and improve image quality on the backend,” said Majumdar. “On their own, meta-optics suffer from poor resolution and color aberrations. But by merging meta-optics with machine learning, we will make a miniaturized imaging system that can produce high-resolution, full-color images with an extended field of view.” [caption id="attachment_24013" align="alignleft" width="441"] Co-investigators Eric Seibel (left) and Karl Böhringer (right)[/caption] To build a miniaturized scope that can be used by surgeons, Majumdar is bringing together a highly diverse team that includes academic researchers, medical professionals and startups all based in Washington State. Majumdar’s co-investigators include nanofabrication expert Karl Böhringer, a professor of electrical & computer engineering and bioengineering at the UW, and Eric Seibel, a professor of mechanical engineering at the UW and expert in ultrathin and flexible medical imaging devices. Machine learning expert Steve Brunton, also a professor of mechanical engineering at the UW, will play a key role in optimizing the meta-optics design. The research team will work with neurovascular and cardiovascular surgeons Drs. Luis Savastano and John Petersen to better understand the needs of their end user. They will also collaborate with UW spinouts Tunoptix, cofounded by Majumdar and Böhringer, and VerAvanti, which is commercializing a technology pioneered by Seibel known as Scanning Fiber Endoscopy (SFE), to figure out how to manufacture and commercialize these scopes at scale. “Current scopes utilize 50-year-old technology consisting of bundles of glass optical fibers that form a rigid tip, limiting where the scopes can go,” said Seibel. “SFE uses lasers and a micro-electro-mechanical scanner to reduce the length of this rigid tip from nine to about five millimeters. Meta-optics will reduce this even further to two and a half millimeters or less, giving surgeons full access to areas of the body that currently cannot be visualized at all.” [caption id="attachment_24015" align="alignright" width="575"] Spectrally Encoded Non-Scanning Endoscopy (SENSE): (a) The current state-of-the art scanning fiber endoscopes can be significantly miniaturized to create a (b) spectrally encoded non-scanning endoscope. (c) The key will be spatial-spectral mapping using dispersive meta-optics, which can be computationally decoded to recover the images.[/caption] Key to the viability of this device in the real world is that it is both inexpensive to produce and disposable. Meta-optics are fabricated using the same semiconductor manufacturing techniques that produce computer chips. “We are fortunate to have access to the largest publicly accessible nanofabrication facility in the Pacific Northwest right here on the UW campus,” said Böhringer, who as the director for the Institute for Nano-engineered Systems oversees the Washington Nanofabrication Facility (WNF), an open-access user facility that provides academic researchers and industry professionals access to nanofabrication tools and expertise. “Microfabrication has successfully been used to mass produce semiconductors, drastically reducing their cost. Certainly, there is a lot of evidence that meta-optics can similarly be made at scale, but it remains an open question exactly how to do this.” The team will begin by working to demonstrate that meta-optics can be used to reduce the tip length of both conventional endoscopes and scanning fiber endoscopes. They also hope to establish the feasibility of their longer-term goal of integrating computational processing and meta-optics to achieve the smallest and most agile endoscope possible. The team’s clinical and industry collaborators will provide input to help ensure a useful and feasible end product. Eventually, clinicians working at the frontiers of endovascular surgery will help test the device. “There are lots of potential applications for meta-optics – everything from cell phone cameras to satellites – but I think that its integration in biomedical devices such as endoscopes is where meta-optics will have the biggest impact,” said Majumdar. “The need is clear. Miniaturization of the optical elements in endoscopes could transform the treatment of cardiovascular disease and ultimately save lives.” [post_title] => UW researchers developing miniaturized imaging device to treat heart attack, stroke [post_excerpt] => [post_status] => publish [comment_status] => closed [ping_status] => closed [post_password] => [post_name] => miniaturized-imaging-device [to_ping] => [pinged] => [post_modified] => 2022-01-18 14:41:26 [post_modified_gmt] => 2022-01-18 22:41:26 [post_content_filtered] => [post_parent] => 0 [guid] => https://www.ece.uw.edu/?post_type=spotlight&p=24012 [menu_order] => 2 [post_type] => spotlight [post_mime_type] => [comment_count] => 0 [filter] => raw ) [2] => WP_Post Object ( [ID] => 23902 [post_author] => 27 [post_date] => 2022-01-10 17:04:08 [post_date_gmt] => 2022-01-11 01:04:08 [post_content] => By Wayne Gillam | UW ECE News [caption id="attachment_23912" align="alignright" width="600"]James Rosenthal holding the NeuroDisc prototype Yang Research Award recipient James Rosenthal holds a NeuroDisc prototype, which he developed at UW ECE in the lab of Professor Matt Reynolds. The NeuroDisc is a low-power wireless brain-computer interface intended for use in electrophysiology experiments. Photo courtesy of Matt Reynolds[/caption] UW ECE congratulates recent graduate James Rosenthal, who was named as the 2021 Yang Research Award recipient for his work developing neurotechnology. Rosenthal received his master’s and doctoral degrees from UW ECE in 2018 and 2021, respectively. He is currently a Marie Curie Postdoctoral Fellow with the Laboratory for Soft Bioelectronic Interfaces at the Ecole Polytechnique Fédérale de Lausanne in Geneva, Switzerland. Rosenthal completed his doctoral degree at UW ECE as a National Science Graduate Research Fellow, and he was advised by UW ECE Associate Professor Matt Reynolds. Rosenthal’s research focuses on the development of wireless brain-computer interfaces. He explores how architectural innovation in embedded systems can reduce the size, weight and power consumption of wireless systems, which in turn can enable new methods for monitoring and treating neurological disorders. “I am extremely honored to be this year’s recipient,” Rosenthal stated in his acknowledgement of the award. “There are many uncertainties throughout a doctoral program, and it is often easy to question the impact of one’s research. To be selected for this award by leading faculty from our department brings me a lot of confidence as I seek to continue my career in research as a professor.”
The purpose of the Yang Research Award is to recognize and encourage outstanding doctoral student research contributions to the field of electrical engineering. The award goes to one qualifying student per year and is open to all doctoral degree candidates in UW ECE. Receiving the award is considered a high honor and helps to create career opportunities for the recipient.
Rosenthal is passionate about teaching, outreach and mentoring. He was the instructor of record for EE417–Wireless Communications in 2020 and 2021, and he was a teaching assistant for over six academic quarters at UW ECE. He served as UW ECE Graduate Student Association co-chair from 2017 to 2018, and he also served as one of two UW ECE graduate and professional student senators for the University from 2018 to 2020. Rosenthal also co-hosted a scientific communication podcast, “The Paperboys,” with fellow UW graduate student Charlie Kelly. Each episode, Rosenthal and Kelly read and discussed the research papers behind headline science news. “James is one of the very top Ph.D. students to have graduated from UW ECE in the last several years,” Reynolds said. “In addition to his many strengths in research, he was an outstanding mentor to the other students working in my lab, and he is also an excellent teacher.” The Yang Research Award was established by successful entrepreneur and former UW ECE faculty member Andrew T. Yang, who spoke at the UW ECE Graduation Celebration in 2012. Yang has been one of the most influential people in the electronic design automation industry for nearly three decades, and he is known for being a visionary in both research and entrepreneurship. When creating the award, Yang stated that he received a similar award when he was a doctoral student and that the recognition gave him the confidence and motivation he needed to continue his promising career in electrical engineering research. Rosenthal has been engaged in groundbreaking research at the doctoral and postdoctoral stages of his academic career. In addition to expressing gratitude for the award, he noted the supportive environment he found at the University of Washington. “The University of Washington offered a unique, interdisciplinary environment to explore my research that would have been difficult to find anywhere else,” Rosenthal stated. “The emphasis on collaborative research, particularly through the Center for Neurotechnology, offered me the opportunity to work alongside leaders in wireless electronics, neuroscience and translational medicine.” [post_title] => James Rosenthal receives Yang Research Award [post_excerpt] => [post_status] => publish [comment_status] => closed [ping_status] => closed [post_password] => [post_name] => rosenthal-yang-research-award [to_ping] => [pinged] => [post_modified] => 2022-01-10 17:04:08 [post_modified_gmt] => 2022-01-11 01:04:08 [post_content_filtered] => [post_parent] => 0 [guid] => https://www.ece.uw.edu/?post_type=spotlight&p=23902 [menu_order] => 3 [post_type] => spotlight [post_mime_type] => [comment_count] => 0 [filter] => raw ) [3] => WP_Post Object ( [ID] => 23833 [post_author] => 26 [post_date] => 2021-12-20 09:23:10 [post_date_gmt] => 2021-12-20 17:23:10 [post_content] =>  

Dear UW ECE Community,

I am excited to introduce the 2021 issue of The Integrator — UW ECE's flagship, annual magazine highlighting the Department's extraordinary faculty research, student achievements, alumni stories, special events and more. Beginning this year, we have transitioned The Integrator into a fully-digital, environmentally sustainable online publication model, made available to our readers as an easily accessible, interactive experience. We hope you enjoy the new format! This has been another extraordinary year for UW ECE. Our faculty, students and alumni continue to do amazing work even in the face of continued challenges to universities and the world-at-large due to the ongoing pandemic. It’s abundantly clear that UW ECE has continued to thrive with its tremendously vibrant and supportive community of individuals coming together to make important, world-changing engineering advances. It is my sincere honor to serve as chair of this outstanding department. I hope you enjoy this year's issue of The Integrator and take some time to read about our phenomenal UW ECE community and its activities. As always, feel free to reach out to us anytime — we’d love to hear from you!

Read The Integrator 2021 here!

  Eric Klavins Eric Klavins Professor and Chair, UW Department of Electrical & Computer Engineering
Check out past issues of The Integrator here. [post_title] => The Integrator 2021 - Focus on Impact - now available! [post_excerpt] => [post_status] => publish [comment_status] => closed [ping_status] => closed [post_password] => [post_name] => the-integrator-2021 [to_ping] => [pinged] => [post_modified] => 2022-01-05 12:47:52 [post_modified_gmt] => 2022-01-05 20:47:52 [post_content_filtered] => [post_parent] => 0 [guid] => https://www.ece.uw.edu/?post_type=spotlight&p=23833 [menu_order] => 6 [post_type] => spotlight [post_mime_type] => [comment_count] => 0 [filter] => raw ) [4] => WP_Post Object ( [ID] => 23771 [post_author] => 25 [post_date] => 2021-12-09 13:43:28 [post_date_gmt] => 2021-12-09 21:43:28 [post_content] => [caption id="attachment_23815" align="aligncenter" width="1024"]Professor Joshua R. Smith was elected into the 2021 class of Fellows of the National Academy of Inventors for his impactful creations in the fields of wireless power, communication, sensing and robotics. Josh Smith, the Milton and Delia Zeutschel Professor in Entrepreneurial Excellence, was elected into the 2021 class of Fellows of the National Academy of Inventors for his impactful creations in the fields of wireless power, communication, sensing and robotics.[/caption] Adapted from article by Rebekka Coakley |  Paul G. Allen School of Computer Science & Engineering Professor Joshua R. Smith, the Milton and Delia Zeutschel Professor in Entrepreneurial Excellence, who holds a joint appointment in the Department of Electrical & Computer Engineering (UW ECE) and the Paul G. Allen School of Computer Science & Engineering, was elected into the 2021 class of Fellows of the National Academy of Inventors (NAI) for his impactful creations in the fields of wireless power, communication, sensing and robotics. Smith, who leads the Sensor Systems Lab, is one of only five University of Washington faculty members to have received this prestigious award that highlights the prolific spirit of innovation in academic inventors. The NAI Fellows program was created to recognize inventors and their contributions to society, which stimulate the economy, improve and save lives, and make the world a better place. It is the highest professional distinction given solely to academic inventors. "Josh has inspired all of us with his leadership in entrepreneurship and innovation," said UW ECE Professor and Chair Eric Klavins. "This is part of why our Department continues to be the number one generator of startup companies at the University. He also models for our students how to create and develop innovative technology that has far-reaching, positive impacts on society. He simply embodies the best of the best." [caption id="attachment_23810" align="alignleft" width="386"]Smith as a graduate student at MIT using mutual capacitance measurements to track hands in the air. Smith as a graduate student at MIT using mutual capacitance measurements to track hands in the air.[/caption] The NAI Fellow selection process considers inventions that have been licensed or commercialized. Smith holds 48 U.S. patents and 16 international patents, 44 of which are licensed by companies. His inventions have led to hundreds of millions of dollars in product revenues, bolstering the economy and the creation of approximately 70 full-time jobs, according to Suzie Pun, a professor in the Department of Bioengineering and another UW faculty member who is an NAI Fellow. His first six patents, developed while a graduate student at MIT, pioneered mutual capacitance sensing and led to the creation of a smart airbag system that was included in every Honda car between 2000 and 2015. Before his arrival at UW, Smith spent five years at Intel Research Seattle, creating new capabilities in wireless power, wireless sensing and robotics. He led the creation of the Wireless Identification and Sensing Platform (WISP), the first fully programmable platform for wireless, battery-free sensing and computation powered by radio waves. Soon after, he developed Wireless Resonant Energy Link (WREL), which uses magnetically coupled resonators to efficiently transfer wireless power even as range, orientation and load vary. With the help of a heart surgeon from Yale, Smith was able to power a ventricular assist device designed for implantation in the human body without requiring a cable through the patient’s chest, called the Free-range Resonant Electrical Energy Delivery System (FREED). This wireless power work at UW is commercialized by WiBotic, a company Smith co-founded with ECE alumnus Benjamin Waters (Ph.D., ‘15). The UW patents are also licensed for implanted heart pumps by Corisma. [caption id="attachment_23805" align="aligncenter" width="1024"]Ambient backscatter devices powered by radio waves from a TV tower in the background. They communicate with one another by selectively reflecting the tower’s radio signals. Ambient backscatter devices powered by radio waves from a TV tower in the background. They communicate with one another by selectively reflecting the tower’s radio signals.[/caption] “Among the many outstandingly inventive engineers at Intel Research Seattle, we were especially excited that Josh joined our faculty, he is extraordinary in every imaginable respect,” said Ed Lazowska, professor and Bill & Melinda Gates Chair Emeritus at the Allen School. “He is an academic inventor and entrepreneur of the highest caliber and in the finest tradition.”
"Invention is just one part of a long process to bring new things into the world. I am very grateful to the many people who have worked so hard to take these inventions from the lab to the world." - Josh Smith
In 2013, Smith, together with Allen School professor Shyam Gollakota and a team of graduate students, developed Ambient Backscatter using existing wireless signals to provide power and communication for low-power sensing and computing devices. This next led to the creation of Passive-Wi-Fi, bringing low-power Wi-Fi to transmissions. They also invented Interscatter, using wireless transmissions over the air from one technology to another for internet-connected implanted devices. Smith also co-led the UW team behind the world’s first battery‐free phone, as well as a series of ultra-low-power battery-free wireless cameras that communicate via backscatter. This research is being commercialized by Jeeva Wireless, a UW spinout co-founded by Smith, Gollakota, and ECE alumni Vamsi Talla (Ph.D., ‘16) and Aaron Parks (Ph.D., ‘17). [caption id="attachment_23776" align="alignleft" width="434"]A robot using non-contact pre-touch sensing to solve the Rubik’s Cube. A robot using non-contact pre-touch sensing to solve the Rubik’s Cube.[/caption] “Josh has a consistent record of impactful inventions,” said Pun. “I have gotten to know him through a research collaboration to develop touchscreen-based sensors for detection of pathogens such as SARS-CoV-2. Josh devised a creative method to improve detection sensitivity for the virus; he is in the process of testing this idea in his laboratory. If successful, his design could be applied for next-generation biosensing devices.” Smith also co-founded Proprio, which provides surgical visualization and navigation, together with UW neurosurgeon Sam Browd, Allen School graduate student Jim Youngquist, UW Foundation board member Ken Denman, and Michael G. Foster School of Business alumnus Gabe Jones (MBA, ‘14). Smith served on advisory councils and task forces for the United States Postal Service and the Smithsonian Institution and is an IEEE Fellow. His work has earned multiple Best Paper Awards, and he is known for his dedicated mentorship of student researchers. “I feel so privileged to collaborate with my outstanding UW faculty and student co-inventors,” said Smith. “And invention is just one part of a long process to bring new things into the world. I am very grateful to the many people who have worked so hard to take these inventions from the lab to the world, including UW CoMotion, many patent attorneys, and most of all the co-founders and employees at the companies making these technologies real.” [caption id="attachment_23801" align="alignright" width="372"] A robot using acoustic levitation to hold a flower without touching it.[/caption] [caption id="attachment_23800" align="alignleft" width="675"] Wireless Ambient Radio Power: A kitchen thermometer is powered by radio waves from the tower in the background.[/caption]
Read the NAI announcement here, and the full list of 2021 Fellows here. Congratulations, Josh! [post_title] => Professor Joshua R. Smith elected Fellow of the National Academy of Inventors for his innovations in wireless power, communication, sensing and robotics [post_excerpt] => [post_status] => publish [comment_status] => closed [ping_status] => closed [post_password] => [post_name] => joshua-smith-nai [to_ping] => [pinged] => [post_modified] => 2021-12-14 09:04:38 [post_modified_gmt] => 2021-12-14 17:04:38 [post_content_filtered] => [post_parent] => 0 [guid] => https://www.ece.uw.edu/?post_type=spotlight&p=23771 [menu_order] => 7 [post_type] => spotlight [post_mime_type] => [comment_count] => 0 [filter] => raw ) [5] => WP_Post Object ( [ID] => 23652 [post_author] => 26 [post_date] => 2021-11-24 10:21:02 [post_date_gmt] => 2021-11-24 18:21:02 [post_content] =>

UW BIOFAB: A force for reproducible science

The UW’s Biofabrication Center, founded by UW ECE Professor and Chair Eric Klavins, partners with Agilent Technologies in pursuit of automated, reproducible research. [caption id="attachment_23658" align="aligncenter" width="907"]Undergraduate technicians perform common molecular biology tasks to enable the rapid design, construction and testing of genetically reprogrammed organisms for biotechnology applications and research. Dennis Wise / University of Washington Undergraduate technicians perform common molecular biology tasks to enable the rapid design, construction and testing of genetically reprogrammed organisms for biotechnology applications and research. Dennis Wise / University of Washington[/caption]   Article by Renske Dyedov, UW Institute for Nano-Engineered Systems (NanoES)

Key to advancing any new scientific discovery is the ability for researchers to independently repeat the experiments that led to it. In science today, particularly biology, the lack of reproducibility between experiments is a major problem that slows scientific progress, wastes resources and time, and erodes the public’s trust in scientific research.

[caption id="attachment_23672" align="alignright" width="232"]UW ECE Professor and Chair, Eric Klavins. Dennis Wise / University of Washington BIOFAB founder, and UW ECE Professor and Chair, Eric Klavins. Dennis Wise / University of Washington[/caption] At the University of Washington, researchers have access to the UW Biofabrication Center, or BIOFAB, a unique facility located in the Nanoengineering and Sciences building in which scientific protocols are encoded as computer programs, allowing undergraduate lab technicians to execute experiments according to detailed instructions. “The BIOFAB is unlike any other lab on campus,” says BIOFAB founder Eric Klavins, Professor and Chair of the UW Electrical & Computer Engineering Department (UW ECE). “In effect, we’ve been able to automate common protocols by using software to assist our student technicians. This ‘human-in-the-loop’ system goes a long way towards improving the replicability of biological research.” In an effort to expand the lab’s automation capabilities, the BIOFAB has partnered with Agilent Technologies Inc., a life sciences development and manufacturing company based in California’s Silicon Valley. Using state-of-the-art research equipment from Agilent, the BIOFAB will develop high-throughput workflows for common tasks of interest to members of the synthetic biology community.

Programming the biology lab

Computer programmers write code to tell a computer what to do and how to do it. For a given program, the same inputs consistently result in the same outputs. In contrast, two biology researchers can seemingly carry out the same experiment, but get different results. This is in part because instructions for how the experiment was conducted – whether documented in a lab notebook or published in a journal – are often vague or incomplete, leaving out details that the author may not have realized impacted the experimental outcome. [caption id="attachment_23657" align="alignleft" width="580"]Aquarium is a web-based software application that allows scientists to build executable protocols, design experimental workflows based on those protocols, manage the execution of protocols in the lab and automatically record the resulting data. Dennis Wise / University of Washington Aquarium is a web-based software application that allows scientists to build executable protocols, design experimental workflows based on those protocols, manage the execution of protocols in the lab and automatically record the resulting data. Dennis Wise / University of Washington[/caption] As a computer scientist turned synthetic biologist, Klavins realized what biologists needed was a more formal way – a programming language – to define how to conduct an experiment. This led to the development of Aquarium, a web-based software application that allows scientists to build executable protocols, design experimental workflows based on those protocols, manage the execution of protocols in the lab and automatically record the resulting data. “Aquarium provides the means to specify, as precisely as possible, how to obtain a result,” said Klavins. When it comes to engineering biology – reprogramming cells to produce chemicals or drugs, or perform complex functions like sensing toxic compounds in the environment – reproducibility is paramount. The BIOFAB uses Aquarium to standardize various scientific workflows, generating reliable and highly reproducible results. The BIOFAB is one of a growing number of labs known as biofoundries which are committed to efficiently engineering biological systems and workflows. BIOFAB operations are overseen by two lab managers, with a dozen or so undergraduate students executing jobs for BIOFAB clients. BIOFAB technicians perform common molecular biology tasks like DNA assembly and purification as a fee-for-service to the scientific community. Since its founding in 2014, the BIOFAB has run over 30,000 jobs for 300+ different clients at the UW and beyond. “The BIOFAB has been absolutely instrumental in establishing and executing robust Aquarium driven protocols for a major portion of our de novo design minibinder pipeline,” said Lance Stewart, Chief Strategy and Operations Officer at the UW’s Institute for Protein Design (IPD). IPD researchers use computers to design millions of minibinders – small, stable proteins that bind with high affinity to targets of interest – that must be produced and tested in the lab. IPD uses the BIOFAB to screen minibinder candidates for protein stability and protein:protein interactions, which involves constructing yeast libraries from chip synthesized oligonucleotide genes encoding minibinder designs and carrying out large scale fluorescence activated cell sorting and next generation DNA sequencing. “By handing off time-consuming wet lab work to our technicians, BIOFAB clients like IPD can focus more on the design and data analysis aspects of their experiments,” said Klavins.

Learning by doing

[caption id="attachment_23659" align="alignright" width="330"]BIOFAB technician Nicole Roullier. Dennis Wise / University of Washington BIOFAB technician Nicole Roullier. Dennis Wise / University of Washington[/caption] On any given day, the BIOFAB is buzzing with undergraduate technicians working together in harmony to complete an assortment of experiments for BIOFAB clients. Most technicians start working in the BIOFAB as freshman or sophomores, and for many, it’s their first real lab experience. Upon joining the lab, BIOFAB lab managers teach students basic lab skills, such as pipetting and sterile technique, and orient them to the lab. Armed with this foundational knowledge, BIOFAB technicians can begin executing a variety of different protocols by following the step-by-step instructions provided through Aquarium. Students become adept at performing complicated experimental workflows involving complex equipment through the process of doing them over and over again. “Aquarium allows us to effectively train many students simultaneously and get them working in the lab relatively quickly,” said Aza Allen, a lab manager at the BIOFAB. “Aquarium’s technician interface makes it easy to get undergraduate students, who do not necessarily know much about molecular biology when they start, to perform experiments reliably.” “I have learned so much beyond what could possibly be taught in a classroom setting,” said BIOFAB technician Nicole Roullier, a UW biochemistry senior. “Most undergraduates don’t have the opportunity to work with such sophisticated equipment and master advanced techniques like qPCR and next-generation sequencing (NGS). This hands-on training has built up my confidence in the lab in preparation for graduate school.”

A promising partnership

The BIOFAB provides critical automation and analytics infrastructure dedicated to enabling the rapid design, construction and testing of genetically reprogrammed organisms for biotechnology applications and research. Through its partnership with Agilent, the BIOFAB aims to offer new high-throughput capabilities that will further speed up and scale up synthetic biology research. “We’re thrilled to be partnering with Agilent,” said Klavins. “Their support will not only accelerate the development of innovative technologies, but will help us educate students using cutting-edge equipment, bolstering our ability to prepare students for success in their own future research and career.” “We think this is the start of an exciting collaboration,” said Kevin Meldrum, General Manager and Vice President of Genomics at Agilent. “We are pleased to be able to support researchers at the UW and the educational mission of the university through the BIOFAB. We see this as an investment in the future of our field.” [caption id="attachment_23660" align="alignleft" width="630"]Agilent’s state-of-the-art liquid-handling robot, the Bravo Automated Liquid Handling platform will help speed up and scale up synthetic biology research. Dennis Wise / University of Washington Agilent’s state-of-the-art liquid-handling robot, the Bravo Automated Liquid Handling platform will help speed up and scale up synthetic biology research. Dennis Wise / University of Washington[/caption] As a result of this partnership, the BIOFAB has acquired several valuable pieces of equipment, including Agilent’s state-of-the-art liquid-handling robot, the Bravo Automated Liquid Handling platform. While the Bravo can be used to automate sample preparation for a variety of different applications, the BIOFAB plans to initially use it to expedite its workflow for NGS. In addition to the Bravo, the BIOFAB has also acquired the AriaMx Real-Time PCR System, and the 5200 Fragment Analyzer System, a parallel capillary electrophoresis system. “Library preparation for high-throughput NGS is a tedious, labor-intensive process,” said Klavins. “Agilent’s Bravo will help make this workflow more efficient and reduce pipetting errors that make results less consistent, while also freeing up time for our technicians to work on less repetitive tasks. We know that there are certainly other workflows that would benefit from the use of Bravo, and we plan to engage BIOFAB users to identify which ones to pursue. We are thrilled to be able to bring this resource to the UW community, and are excited to see the compelling science that comes out as a result.” [post_title] => UW BIOFAB: A force for reproducible science [post_excerpt] => [post_status] => publish [comment_status] => closed [ping_status] => closed [post_password] => [post_name] => uw-biofab-a-force-for-reproducible-science [to_ping] => [pinged] => [post_modified] => 2021-12-07 12:39:27 [post_modified_gmt] => 2021-12-07 20:39:27 [post_content_filtered] => [post_parent] => 0 [guid] => https://www.ece.uw.edu/?post_type=spotlight&p=23652 [menu_order] => 8 [post_type] => spotlight [post_mime_type] => [comment_count] => 0 [filter] => raw ) ) [post_count] => 6 [current_post] => -1 [in_the_loop] => [post] => WP_Post Object ( [ID] => 24042 [post_author] => 27 [post_date] => 2022-01-21 17:26:53 [post_date_gmt] => 2022-01-22 01:26:53 [post_content] => By Wayne Gillam | UW ECE News [caption id="attachment_24047" align="alignright" width="625"]Optical GAN illustration An illustration of the UW ECE-led research team’s integrated optical computing chip and “handwritten” numbers it generated. The chip contains an artificial neural network that can learn how to write like a human in its own, distinct style. This optical computing system uses “noise” (stray photons from lasers and thermal background radiation) to augment its creative capabilities. The system is also approximately 10 times faster than comparable conventional digital computers and more energy efficient, helping to put AI and machine learning on a path toward environmental sustainability. Illustration by Changming Wu[/caption] Artificial intelligence and machine learning are currently affecting our lives in a myriad of small but impactful ways. For example, AI and machine learning applications help to interpret voice commands given to our phones and electronic devices, such as Alexa, and recommend entertainment we might enjoy through services such as Netflix and Spotify. In the near future, it’s predicted that AI and machine learning will have an even larger impact on society through activities such as driving fully autonomous vehicles, enabling complex scientific research and facilitating medical discoveries. But the computers used for AI and machine learning demand energy, and lots of it. Currently, the need for computing power related to these technologies is doubling roughly every three to four months. And cloud computing data centers used by AI and machine learning applications worldwide are already devouring more electrical power per year than some small countries. Knowing this, it’s easy to see that this level of energy consumption is unsustainable, and if left unchecked, will come with serious environmental consequences for us all. UW ECE Professor Mo Li and graduate student Changming Wu have been working toward addressing this daunting challenge over the last couple of years, developing new optical computing hardware for AI and machine learning that is faster and much more energy efficient than conventional electronics. They have already engineered an optical computing system that uses laser light to transmit information and do computing by using phase-change material similar to what is in a CD or DVD-ROM to record data. Laser light transmits data much faster than electrical signals, and phase-change material can retain data using little to no energy. With these advantages, their optical computing system has proven to be much more energy efficient and over 10 times faster than comparable digital computers. [caption id="attachment_24048" align="alignright" width="400"]Mo Li and Changming Wu headshots UW ECE Professor Mo Li (left) and UW ECE graduate student Changming Wu (right) led the interdisciplinary, multi-institutional research team that built this optical computing system. Mo Li photo by Ryan Hoover[/caption] Now, Li and Wu are addressing another key challenge, the ‘noise’ inherent to optical computing itself. This noise essentially comes from stray light particles, photons, that interfere with computing precision. These errant photons come from the operation of lasers within the device and background thermal radiation. In a new paper published on Jan. 21 in Science Advances, Li, Wu and their research team demonstrate a first-of-its-kind optical computing system for AI and machine learning that not only mitigates this noise but actually uses some of it as input to help enhance the creative output of the artificial neural network within the system. This work resulted from an interdisciplinary collaboration of Li’s research group at the UW with computer scientists Yiran Chen and Xiaoxuan Yang at Duke University and material scientists Ichiro Takeuchi and Heshan Yu at the University of Maryland. “We’ve built an optical computer that is faster than a conventional digital computer,” said Wu, who is the paper’s lead author. “And also, this optical computer can create new things based on random inputs generated from the optical noise that most researchers tried to evade.”

Using noise to enhance AI creativity

Artificial neural networks are bedrock technology for AI and machine learning. These networks function in many respects like the human brain, taking in and processing information from various inputs and generating useful outputs. In short, they are capable of learning. In this research work, the team connected Li and Wu’s optical computing core to a special type of artificial neural network called a Generative Adversarial Network, or GAN, which has the capacity to creatively produce outputs. The team employed several different noise mitigation techniques, which included using some of the noise generated by the optical computing core to serve as random inputs for the GAN. The team found that this technique not only made the system more robust, but it also had the surprising effect of enhancing the network’s creativity, allowing it to generate outputs with more varying styles.
"This optical system represents a computer hardware architecture that can enhance the creativity of artificial neural networks used in AI and machine learning, but more importantly, it demonstrates the viability for this system at a large scale where noise and errors can be mitigated and even harnessed. AI applications are growing so fast that in the future, their energy consumption will be unsustainable. This technology has the potential to help reduce that energy consumption, making AI and machine learning environmentally sustainable and very fast, achieving higher performance overall." — UW ECE Professor Mo Li
To experimentally test the image creation abilities of their device, the team assigned the GAN the task of learning how to handwrite the number “7” like a human. The optical computer could not simply print out the number according to a prescribed font. It had to learn the task much like a child would, by looking at visual samples of handwriting and practicing until it could write the number correctly. Of course, the optical computer didn’t have a human hand for writing, so its form of “handwriting” was to generate digital images that had a style similar to the samples it had studied but were not identical to them. “Instead of training the network to read handwritten numbers, we trained the network to learn to write numbers, mimicking visual samples of handwriting that it was trained on,” Li said. “We, with the help of our computer science collaborators at Duke University, also showed that the GAN can mitigate the negative impact of the optical computing hardware noises by using a training algorithm that is robust to errors and noises. More than that, the network actually uses the noises as random input that is needed to generate output instances.” After learning from handwritten samples of the number seven, which were from a standard AI-training image set, the GAN practiced writing “7” until it could do it successfully. Along the way, it developed its own, distinct writing style. The team was also able to get the device to write numbers from one to 10 in computer simulations. As a result of this research, the team was able to show that an optical computing device could power a sophisticated form of artificial intelligence, and that the noise inherent to integrated optoelectronics was not a barrier, but in fact could be used to enhance AI creativity. They also showed that the technology in their device was scalable, and that it would be possible for it to be deployed widely, for instance, in cloud computing data centers worldwide. Next steps for the research team will be to build their device at a larger scale using current semiconductor manufacturing technology. So, instead of constructing the next iteration of the device in a lab, the team plans to use an industrial semiconductor foundry to achieve wafer-scale technology. A larger scale device will further improve performance and allow the research team to do more complex tasks beyond handwriting generation such as creating artwork and even videos. “This optical system represents a computer hardware architecture that can enhance the creativity of artificial neural networks used in AI and machine learning, but more importantly, it demonstrates the viability for this system at a large scale where noise and errors can be mitigated and even harnessed,” Li said. “AI applications are growing so fast that in the future, their energy consumption will be unsustainable. This technology has the potential to help reduce that energy consumption, making AI and machine learning environmentally sustainable — and very fast, achieving higher performance overall.” This research is financially supported by the Office of Naval Research and the National Science Foundation. For more information, contact Mo Li. [post_title] => Harnessing noise in optical computing for AI [post_excerpt] => [post_status] => publish [comment_status] => closed [ping_status] => closed [post_password] => [post_name] => using-noise-optical-computing-for-ai [to_ping] => [pinged] => [post_modified] => 2022-01-21 17:26:53 [post_modified_gmt] => 2022-01-22 01:26:53 [post_content_filtered] => [post_parent] => 0 [guid] => https://www.ece.uw.edu/?post_type=spotlight&p=24042 [menu_order] => 1 [post_type] => spotlight [post_mime_type] => [comment_count] => 0 [filter] => raw ) [comment_count] => 0 [current_comment] => -1 [found_posts] => 785 [max_num_pages] => 131 [max_num_comment_pages] => 0 [is_single] => [is_preview] => [is_page] => [is_archive] => 1 [is_date] => [is_year] => [is_month] => [is_day] => [is_time] => [is_author] => [is_category] => [is_tag] => [is_tax] => [is_search] => [is_feed] => [is_comment_feed] => [is_trackback] => [is_home] => [is_404] => [is_embed] => [is_paged] => [is_admin] => [is_attachment] => [is_singular] => [is_robots] => [is_posts_page] => [is_post_type_archive] => 1 [query_vars_hash:WP_Query:private] => c64914061c8ecf9b16abe746203f6ad7 [query_vars_changed:WP_Query:private] => 1 [thumbnails_cached] => [stopwords:WP_Query:private] => [compat_fields:WP_Query:private] => Array ( [0] => query_vars_hash [1] => query_vars_changed ) [compat_methods:WP_Query:private] => Array ( [0] => init_query_flags [1] => parse_tax_query ) ) )
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