It will likely be the first of its kind. In the world.
“No one else, to our knowledge, has tried to put such a high-speed computing cluster so close to the equipment in their labs,” says Yaling Liu, a professor of bioengineering and mechanical engineering and mechanics in Lehigh University’s P.C. Rossin College of Engineering and Applied Science. “We believe the facility we’re proposing will have a huge impact on our campus by enabling big-data analysis and new science.”
Liu leads the Lehigh team that recently received a nearly $1 million grant to fund the development of a heterogeneous edge computing platform for real-time scientific machine learning. The award is part of the National Science Foundation’s Major Research Instrumentation program (MRI), which supports the development or acquisition of equipment that will advance science and engineering. One of the goals of such grants is to significantly impact research in a broad range of areas.
Joshua Agar, an assistant professor of mechanical engineering and mechanics at Drexel University, is also a member of the team, along with additional Lehigh contributors Lifang He, an assistant professor of computer science and engineering; Wujie Wen, an assistant professor of electrical and computer engineering; and Yue Yu, an associate professor of applied mathematics in Lehigh’s College of Arts and Sciences.
Heterogeneous computing is a technique in which different processors are applied toward the execution of specific tasks, resulting in gains in performance or efficiency or both, while edge computing refers to the ability to process data as close to the data source as possible. Combining the two will address several critical computing needs.
“Computer clusters are typically located far from the equipment that generates the data,” says Liu. “And that distance affects the speed by which data can be analyzed. So what ends up happening is that you collect all this data, and then you have to spend days analyzing it, after the fact. And if something went wrong with the experiment, you have to go back and repeat it, which can take hours or days, and can be expensive if you’re paying hourly to use these machines.”
Data storage poses another problem. Equipment like high-speed microscopes generate a huge amount of information, which means researchers like Liu are forced to keep their experiments short.
And finally, because of the lag between collection and analysis, the current system doesn’t allow researchers to purge redundant or wasteful data. So valuable storage is sometimes taken up by information that’s of no interest to them.
The proposed development of a heterogenous edge computing platform for real-time scientific machine learning will circumvent latency and bandwidth challenges, and allow for real-time analysis and control of optical, scanning probe, and transmission electron microscopy, thanks to its proposed location. Liu says the server will eventually be installed in the basement of the Health, Science, and Technology Building, with high-speed fibers connecting it to various basement labs housing the microscopes.
The platform will allow researchers across the university to make advances in fields like wireless communication, health care monitoring, bioinformatics, advanced manufacturing, and multiagent autonomous systems, and will facilitate interdisciplinary teams pursuing novel research directions.
For Liu, it could ultimately mean getting vital information to patients, faster.
Liu leads Lehigh’s Bio-Nano Interface Lab, where he and his team currently use the high-speed camera of an optical microscope to sort tumor cells from normal cells. But with each blood sample containing hundreds of millions of cells, the researchers are limited by how fast they can sort the cells and how long their experiments can last. And they’re unable to make decisions in the moment about what they’re seeing.
“My lab is interested in the biomedical application of this platform,” he says. “We want to make real-time decisions based on the image as to whether something is a tumor cell or a healthy white blood cell. That’s what this facility is ultimately going to make possible. With this high-speed, edge computing, we’ll be able to decide, ‘This is a target cell,’ and then we can collect it out immediately, and do further culturing or precision-based medicine based on those sorted cells.”
What may be the world’s first such computing platform configuration will most definitely be a game changer for him and his research.
“We’ll be able to do continuous, real-time analysis that will enable us to finish our tasks in one day, rather than over several months,” he says. “That will allow us to advance our research, which is incredibly exciting.”
About Yaling Liu
Yaling Liu, a professor of bioengineering and mechanical engineering and mechanics in Lehigh University’s P.C. Rossin College of Engineering and Applied Science, conducts interdisciplinary research in micro/nanoengineering for biology and medicine. In particular, he focuses on using combined experimental and computational approaches to characterize the interfacial phenomena at the micro/nano scale and in biological systems. Current efforts in his lab focus on emerging applications in microfluidics, organ-on-chip devices, nanomedicine, biosensing, and micro/nanofabrication. These involve multiscale modeling, biofluid mechanics, image-based simulation, MEMS fabrication, microfluidic chip testing, cell manipulation, and integration with machine learning. He is a recipient of the NSF Early CAREER Development Award and Fellow of ASME.
About Joshua Agar
Joshua C. Agar is an assistant professor in the Department of Mechanical Engineering and Mechanics at Drexel University. Prior to Drexel, Joshua was an assistant professor in the Department of Materials Science and Engineering at Lehigh University. Joshua earned a BS from the University of Illinois at Urbana-Champaign, an MS from the Georgia Institute of Technology, and a PhD from the University of Illinois at Urbana Champaign in materials science and engineering. Following his degrees, he was a postdoctoral scholar in machine learning at the University of California, Berkeley. He has broad research interests spanning synthesis and characterization of multifunctional materials, multimodal characterization and spectroscopy techniques, physics-informed and constrained machine learning methods, and codesign of machine learning models for real-time machine learning on heterogeneous computing. He applies these techniques to design, understand, and fabricate functional materials with applications in sensing, energy conversion, and computing.
About Lifang He
Prior to joining the computer science and engineering faculty at Lehigh University, where she is currently an assistant professor, Lifang He was a postdoctoral associate in the Department of Biostatistics, Epidemiology and Informatics within the Perelman School of Medicine at University of Pennsylvania, as well as the Weill Cornell Medical College of Cornell University. Her research interests include machine learning/deep learning, data mining, tensor analysis, and biomedical informatics. She holds a BS in computational mathematics from Northwest Normal University and a PhD in computer science from South China University of Technology.
About Yue Yu
Yue Yu is an associate professor in the Department of Mathematics in Lehigh University’s College of Arts and Sciences. She is also affiliated with Lehigh’s College of Health and the university’s Institute for Data, Intelligent Systems, and Computation (I-DISC). Her research concerns topics in the areas of numerical analysis, scientific computing and machine learning, where she works on the development of novel numerical tools for models with background in science, engineering, and biomedicine. She is particularly interested in applying her mathematical analysis knowledge in the design and analysis of mathematical models and numerical schemes.
About Wujie Wen
Wujie Wen is an assistant professor in the Department of Electrical and Computer Engineering at Lehigh University. He received his PhD from the University of Pittsburgh in 2015. He was an assistant professor in the ECE department of Florida International University (Miami, FL) during 2015-2019. Before joining the academy, he also worked with AMD and Broadcom for various engineer and intern positions. His research interests include deep learning hardware acceleration/security/application, neuromorphic computing, electronic design automation (EDA), and circuit-architecture design for emerging memory technologies etc. His work has been published widely across venues in EDA and machine learning/AI, including HPCA, DAC, ICCAD, DATE, ICPP, HOST, USENIX Security, ACSAC, CVPR, NIPs, ECCV, and AAAI. Wen is the associate editor of Neurocomputing and serves/served as the General Chair of ISVLSI 2019 (Miami), Technical Program Chair of ISVLSI 2018 (Hong Kong), as well as program committee for many conferences such as DAC, ICCAD, DATE, and ASP-DAC. He received best paper nominations from ICCAD2018, ASP-DAC2018, DATE2016, and DAC2014. He was also the recipient of the 49th DAC A. Richard Newton Graduate Scholarship—the most prestigious PhD scholarship (one awardee per year) in EDA society, the 2014 Bronze Medal of ACM Special Interest Group on Design Automation (SIGDA) student research competition (SRC) in ICCAD, and the 2015 DAC PhD forum best poster presentation. His research projects are currently sponsored by NSF, AFRL, and the Florida Center for Cybersecurity.
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