Information Processing Circuits and Systems
Past few decades have seen unprecedented growth in the raw computing capabilities of electronic systems such as desktops, laptops, mobiles phone etc. This emergence of advanced data processing systems has revolutionized several industries and has led to the availability of vast amount of digital data. Recent advances in machine learning and big data areas explore the ways of deriving useful conclusions from the available data but at a cost of significant increase in data processing requirement and hence much higher computation energy consumption. At the same time, prospects of further improvements in CMOS transistor, a workhorse of state-of-the-art computing systems, have started to diminish, thanks to prohibitive increase in its leakage power and power density. Hence, it has now become crucial to ask, “what is the best way to build information processing systems for the future?”. This session invites researchers working on addressing various aspects of this question, including but not limited to, advances in state-of-the-art digital and analog CMOS-based designs, ways of addressing challenges such as high device variability and leakage power, alternative computing paradigms such as bio-neuro-inspired computing, computing using beyond-CMOS devices, quantum computing etc.
Prof. Kwabena Boahen, Stanford University
Future of Computing
Prof. Kwabena Boahen
Prof. Vikram Adve
Prof. Rakesh Kumar
Invited Student Speaker
Edward Lee, Stanford University
Space-time Computing for Machine Learning
UIUC Student Speakers
Mingu Kang, Compute Memory: An Energy-efficient Memory-intensive Computation for Inference
Glenn Ko, Sampling-based Inference Architecture for Machine Learning Tasks
Yingyan Lin, Variation-tolerant Architectures for Convolutional Neural Networks in the Near Threshold Voltage Regime
Wei Zuo, Accurate High-level Modeling and Automated Hardware/Software Co-design for Effective SoC Design Space Exploration
Mingu Kang won the Best Student Talk award for this session, and was awarded with Amazon gift card.