Published on 7 October 2020
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 ©CEA
Edge AI Program Manager, CEA-Leti
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Schedule Time
5:15 p.m: Artificial Intelligence: Hardware as a game changer
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ABSTRACT
Resistive random access memory (RRAM) technologies, often referred to as memristors, hold fantastic promise for implementing novel in-memory computing systems for massively parallel, low-power and low-latency computation.This talk will first present the role of RRAM to enable the hardware implementation of Spiking Neural Networks (SNN). The resistive memories technologies can play a crucial role in the hardware implementation of the three main SNN building blocks: learning and memory, communication and computation. Second, we will present a new path towards realizing intelligent systems, compatible with fundamental resistive memory properties, particularly cycle-to-cycle variability, to bring learning to the edge.
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BIO
Elisa is a senior
scientist at CEA-Leti. She joined the institute in 2011 after spending one year
on the research staff at Fondazione Bruno Kessler, Trento. Her current research
interests concern the development of new technologies for bio-inspired
neuromorphic computing, with special focus on resistive switching memory
devices (RRAM) and phase change memory (PCM). She has authored or co-authored 4
book chapters and more than 100 technical papers.
She is coordinator of the "MeM-Scales" (2020-2022) European project (H2020) focused on the codevelopment of a novel class of algorithms, devices and circuits that reproduce multi-timescale processing of biological neural systems.
She also is associate editor of the APL special issue on Emerging Materials in Neuromorphic Computing (February 2020) and of the incoming IEEE Transactions on Circuits and Systems –II (2020-2021).She received the PhD in Electrical Engineering from
the Università degli Studi di Udine (Italy) and the Grenoble Institute of
Technology (INPG, France) in 2010.
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