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MATRIX Spring Seminar Series – Dr. Ethan Ahn
January 28, 2022 • 11:00 am - 12:00 pm
Hardware Implementation of Machine Learning by Emerging Nanodevices
Ethan C. Ahn
Assistant Professor of Electrical & Computer Engineering
The University of Texas at San Antonio
https://utsa.zoom.us/j/92387759081
Friday, January 28, 2022
11 AM – 12 PM CST
The conventional scaling of today’s silicon-CMOS technology cannot satisfy the performance and energy-efficiency needs of abundant-data applications, as they require real-time analytics on enormous quantities of user data. Therefore, research is essential on novel computing hardware to tackle the fundamental limitation of the conventional von Neumann computing (“memory bottleneck”) and further enable emerging applications in e-commerce transactions, financial or business decisions, information security, defense, and forecasting of retail trends and demands. Neuromorphic (bio- or brain-inspired) computing is an emerging computing paradigm that takes inspiration from the way the brain computes. While computing systems based on the classical von Neumann architecture have one or more central processing units physically separated from memory, biological information processing features a co-localized memory and logic system where memory is distributed with the processing. Because the neural networks are extremely efficient at certain tasks that require massive amounts of data to be processed (e.g., recognition, classification, and prediction), research on neuromorphic computing is strongly driven by the need to invent a new computing topology that can lead to great improvements in computing efficiency in these machine learning tasks. In this talk, cutting-edge research on emerging nanoscale memory devices such as resistive random access memory (RRAM), phase-change memory (PCM), and spin-transfer-torque magnetic random access memory (STT-MRAM) is introduced in the context of hardware implementation of machine learning. Future perspectives on these emerging device technologies will also be discussed.