Event
Booz Allen Hamilton Colloquium: Catherine Graves, Hewlett Packard
Friday, February 11, 2022
3:30 p.m.-4:30 p.m.
VIRTUAL
Darcy Long
dlong123@umd.edu
https://ece.umd.edu/events/distinguished-colloquium-series
Speaker: Catherine Graves
Principal Research Scientist
Hewlett Packard
Title: "Building brain-inspired hardware accelerators: co-designing devices, circuits and architectures for machine learning and pattern matching applications"
RSVP FOR ZOOM DETAILS:
https://go.umd.edu/catherineg
Abstract: The dramatic rise of data-intensive workloads has revived special-purpose hardware for continuing gains in computing performance. Several promising special-purpose approaches take inspiration from the brain, which outperforms digital computing in power and performance in key tasks such as pattern matching. One brain-inspired architecture called “in-memory computing” significantly reduces data movement and has been shown to improve performance in CMOS ASIC demonstrations. However, these approaches still suffer from low power efficiency. Emerging non-volatile memories are a highly attractive alternative for low-power and high-performance in these architectures. Originally developed as digital (binary) non-volatile memories, many of these devices have a highly tunable analog resistance which are well-matched to in-memory computing architectures. I will review our team’s recent work using crossbar and content addressable memory (CAMs) circuits to accelerate important computing workloads in machine learning, complex pattern matching and combinatorial optimization. I will also discuss our team’s recently invented analog CAM circuit targeted to accelerate interpretable machine learning models.In this talk, hear about efforts at Hewlett Packard Labs to co-design from circuits and devices to algorithms and architectures - enabling low power, high-throughput computation for important computing workloads.
Bio: Dr. Graves is a Principal Research Scientist at Hewlett Packard Labs developing analog and neuromorphic computational accelerators which leverage emerging devices such as resistive RAM for high energy efficiency and throughput compared to general-purpose digital approaches in data-centric domains. Some of her previous work utilized multilevel analog resistive RAM devices to natively perform matrix multiplication within crossbars, accelerating a core computation of wide-ranging applications from neural networks to signal processing. Currently, she leads a research team exploring uses of RRAM-based and analog CAM circuits for accelerating diverse computational models, including tree-based ML models and finite automata processing for network security and genomics applications. Cat received her Ph.D. in Applied Physics from Stanford University studying ultrafast magnetism for future magnetic memory technologies while an NSF Graduate Research Fellow. She has published over 30 peer-reviewed papers, three book chapters, and has been awarded 14 US patents.
The Electrical and Computer Engineering Distinguished Colloquium Series hosted by Booz Allen Hamilton features distinguished speakers from across the nation and around the globe, and also provides venues in which ECE faculty can showcase their research to a broad audience of their colleagues and students, as well as friends of the university.
