May 7
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3:15 PM
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4:15 PM
Machine Learning for In-Sensor Artificial Intelligence and High-performance Circuit Design
Abstract: This talk focuses on application of machine-learning (ML) for imparting intelligence to sensing devices as well as lead to high-performance circuit design. As wireless sensors are more widely adopted, the volume of data produced by these devices are expected to reach thousands of petabytes/month. Transmitting this large volume of data over the cloud for processing will potentially emerge as a communication bottleneck and increase latency of decisions. Transmitting naively all data generated by a wearable medical device is also costly in terms of power/energy- transmitter is usually the highest consumer of energy in a sensor (at least 10~20x more energy than sensing). Key to addressing this data deluge is to increase capabilities sensing devices to process information locally and have on-device inference capabilities, such as through embedding AI capabilities into the wearable device that will allow extraction of key information from the sensor data. There needs to be balance between what can be processed locally on-device with low power/energy and how to optimally decide the volume of data communication from the device (to cloud as an example). The barriers to this approach lie in the computational complexity of AI algorithms that makes it challenging to fit AI models on wearables with limited resources. Some of the answers might lie in going back to early days of signal processing in silicon – developing analog circuit techniques for AI development which will require collaborative innovations in both AI model development and analog circuit design techniques. In this talk, I will present our research on developing analog AI circuits and their demonstrations with use cases from health monitoring to IoT.
The second part of this talk will present ML approaches for enhancing performance of data converters. ML has the potential to emerge as an alternative to current signal processing based complex calibration algorithms for enhancing data converter performance in advanced processes. By learning an efficient representation of the input and data converter behavior, a simple neural network can correct data converter errors arising from multiple sources of non-idealities with similar accuracy as complex calibration algorithms but with a much lower hardware cost.
Arindam Sanyal
Bio: Arindam Sanyal is currently an assistant professor in the School of Electrical, Computer and Energy Engineering at Arizona State University. Prior to this, he was an analog design engineer with Silicon Laboratories and assistant professor in State University of New York. He received his PhD in Electrical and Computer Engineering from the University of Texas at Austin in 2015, his M.Tech from The Indian Institute of Technology, Kharagpur in 2009 and B.E from Jadavpur University, India in 2007. Dr. Sanyal’s research expertise includes analog/mixed signal design, bio-medical sensor design, hardware security and neuromorphic computing. He serves in technical program committees for Custom Integrated Circuits Conference (CICC), Design Automation Conference (DAC), International Conference on Computer-Aided Design (ICCAD), VLSI Test Symposium (VTS), Analog Signal Processing Technical Committee (ASP-TC), and VLSI Systems and Applications Technical Committee (VSA-TC) within IEEE Circuits and Systems society, and VLSI-D.