Multilevel memristive switching devices for efficient analog In-memory AI

Oxide based multilevel memristive switching devices for efficient analog In-memory computing in Artificial Intelligence applications
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Dr. Glenn Ning Ge
CEO, TetraMem Inc
TetraMem's memristive devices-based analog in-memory computing significantly boosts throughput and energy efficiency in deep learning. Our approach utilizes pre-trained synaptic weights from cloud-based training, directly programming them into computing memristors/multi-level RRAMs made with nanometer thin-films for edge deployment and enabling post-tuning to accommodate specific scenarios.
High-precision programmability ensures uniform performance across memristive networks by necessitating numerous distinguishable conductance levels in each device. This advancement benefits applications like neural network training and inference computing.
By achieving stable 8 bits and above multi-levels conductance in individual memristor devices (up to 11 bits/cell, as featured in "Nature" main journal publication, Mar 2023), we enable monolithically integrated semiconductor chips, featuring large crossbar arrays on complementary metal-oxide-semiconductor (CMOS) circuits in the commercial foundry, suitable for diverse AI applications. Our arbitrary precision computing based on analog computing work is published with "Science" main journal in Feb 2024.
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Bio:
Dr. Glenn Ning Ge
is the CEO and co-founder of TetraMem, a leading Silicon Valley startup
With a decade of experience in the semiconductor sector, he has contributed to numerous product innovations. He boasts around 800 global patent filings, stemming from over 300 US/PCT patent families, many of which are now in mass production.
Dr. Ge holds three Master’s degrees, including an MBA from the University of Michigan's Ross School of Business, and a Ph.D. in Electrical Engineering from Nanyang Technological University, Singapore.
Co-sponsored by: CH06083 – SCV/SF Jt. Section Chapter, ED15
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