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Multilevel memristive switching devices for efficient analog In-memory AI

May 23 @ 11:30 am - 1:15 pm PDT

San Francisco Bay Area IEEE Nanotechnology Council

San Francisco Bay Area IEEE Nanotechnology Council

2020, 2017 & 2014 Nanotechnology Council Outstanding Chapter (world-wide)

2019, 2016 & 2014 IEEE Outstanding Chapter (Western USA)

2019, 2016 IEEE Outstanding Chapter (Santa Clara Valley)

http://sites.ieee.org/sfbanano

Oxide based multilevel memristive switching devices for efficient analog In-memory computing in AI applications

Dr. Glenn Ning Ge

CEO, TetraMem Inc

In-Person Meeting

Thursday, May 23, 2024

11:30 AM: Networking, Pizza & Drinks

Noon — 1 pm: Seminar

Please register on Eventbrite before 9:30 AM on Thursday, May 23, 2024

Walk-In attendance is welcomed but discouraged

Please assist us in our event planning!

If you decide not to attend… – please cancel reservations by 8:00 AM on Thursday, ** Tickets cancelled by 8 AM on May 23 will have payments refunded*** Note: Eventbrite Fees will not be refunded

Location:

EAG Laboratories;

810 Kifer Road, Sunnyvale

==> Use corner entrance: Kifer Road / San Lucar Court

==> Do not enter at main entrance on Kifer Road

(Parking: on street or in parking lot behind EAG)

Abstract:

The von Neumann architecture’s intrinsic bottleneck in data transfer between processor and memory units hinders performance as data sets continue to grow.

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.

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.

If you have questions or problems with your registration, please contact LincolnBourne@gmail.com

* Please help us manage our event planning. When we have many walk-in attendees, it is difficult for us to order the proper amount of food for lunch.

** Tickets cancelled by 8 AM on May 23 will have payments refunded*** Note: Eventbrite Fees will not be refunded

Venue

EAG Laboratories
810 Kifer Road, ==> Use corner entrance: Kifer Rd. / San Lucar Ct.=> Do not use main entrance on Kifer Rd.
Sunnyvale, CA 94086 United States

Venue

EAG Laboratories
810 Kifer Road, ==> Use corner entrance: Kifer Rd. / San Lucar Ct.=> Do not use main entrance on Kifer Rd.
Sunnyvale, CA 94086 United States
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