MATRIX Spring Seminar Series – Dr. Ethan Ahn

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.

LINK

MATRIX Spring Seminar Series – Dr. Ayse Coskun

AI for Efficient and Resilient Computing

Ayse Coskun

Boston University

 https://utsa.zoom.us/j/92387759081

Friday, February 4, 2022

11 AM – 12 PM CST

Today’s computers, ranging from small-scale embedded to large-scale systems, face challenges in delivering predictable performance, while maintaining efficiency, resilience, and security. Much of computer system management has traditionally relied on (manual) expert analysis and policies that rely on heuristics derived based on such analysis. This talk will demonstrate a new approach on designing “automated analytics” methods for computing systems, leading the path towards a longer-term vision where computing systems are able to self-manage and improve. Specifically, the talk will first cover how to systematically diagnose root causes of performance variations (or performance “anomalies”), which cause substantial efficiency losses and higher cost. Second, it will discuss how to identify applications running on computing systems and discuss how such discoveries can help reduce vulnerabilities and avoid unwanted applications. The talk will highlight how to apply machine learning to help understand and improve complex systems and show several pathways and challenges in real-world deployment of AI-based methods.

LINK