The Future of Energy-Efficient AI with Memristor Technology
The Future of Energy-Efficient AI with Memristor Technology
Who will you meet ?
Erin Vogt
Senior Associate Director, Graduate Recruitment & Admission
The George Washington University, School of Engineering and Applied Science Erin Vogt; Senior Associate Director, Graduate Recruitment & Admission Erin manages graduate recruitment and admissions operations for the School of Engineering and Applied Science. With 10+ years of experience in higher and international education, Erin is passionate about helping students find the best-fit university for their personal and professional goals. Erin has a B.A. in Intercultural Communication from the University of Pittsburgh and an M.A. in International Affairs from Florida State University.Dr. Gina Adam
Associate professor with the Electrical and Computer Engineering Department
The George Washington University, School of Engineering and Applied Science Gina Adam is an associate professor with the Electrical and Computer Engineering department at the George Washington University. Her group works on the development of emerging non-volatile memory devices and novel hardware foundations that will enable new ways of neuro-inspired computing. She received her Ph.D. in electrical and computer engineering from the University of California Santa Barbara in 2015 and was a research scientist at the Romanian National Institute for Research and Development in Microtechnologies and a visiting scholar at École Polytechnique Fédérale de Lausanne before joining GWU. She was the recipient of an International Fulbright Science and Technology award in 2010, a Mirzayan fellowship at the National Academy of Engineering in 2012, a H2020 Marie Sklodowska-Curie grant from the European Commission in 2016, a NSF CRII award in 2020, AFOSR YIP and NSF CAREER awards in 2023 and a DOE Early Career Award in 2024. She also received the GW Engineering Outstanding Early Career Teaching Award (2022), a GW-wide Morton A. Bender Teaching Award (2023) and the NAGS Teaching Award and the IEEE HKN Teaching Award in 2024.Artificial intelligence is expected to require increasingly massive amounts of computing resources in the coming decades at significant financial and environmental costs. New hardware technologies are necessary to keep up with the major demand in complexity and energy efficiency of these algorithms. Brain-inspired computing is very promising, since the brain is capable of continuously learning from new data, while consuming only a small fraction of energy that would be consumed in artificial intelligence systems. The reason could be that current artificial intelligence systems have limited biomimicry, as exemplified by deep neural networks with simplistic neuronal and synaptic dynamics. Their training is usually executed in microprocessors, graphics processing units or tensor processing units based on existing digital hardware technologies with poor energy efficiency for these tasks. By comparison, emerging analog hardware technologies, like memristors, show promise for dense energy efficient systems given their ultra-scalable footprint and better energy/bit consumption. In this talk, I will describe the basics of memristors, as well as opportunities and challenges of achieving practical adoption. I will also summarize our interdisciplinary efforts across the innovation stack, from new types of complex materials and synaptic devices to new types of prototyping systems and brain-inspired algorithms.
Interdisciplinary efforts involved in advancing AI hardware
Future of AI Hardware
Energy Efficiency in AI Systems
Q&A session
Interdisciplinary efforts involved in advancing AI hardware
Future of AI Hardware
Energy Efficiency in AI Systems
Q&A session
Expert career advice
Job search strategies
International student support
Success stories
Expert career advice
Job search strategies
International student support
Success stories
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27 February 2025
9:00 AM EST (America/New_York)
15 January 2025
10:30 AM EST (America/New_York)
08 January 2025
9:30 AM EST (America/New_York)
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