Analog In-Memory Computing With Multilevel Resistive Switching Devices

This talk will discuss TetraMem’s solution to data transfer bottlenecks between processing and memory units, which is important for applications relying on large datasets e.g. IoT

Abstract

Conventional digital processors based on the von Neumann architecture have an intrinsic bottleneck in data transfer between processing and memory units. This constraint increasingly limits performance as data sets continue to grow exponentially for the various applications, for example, IoT, AR/VR wearables, automotive and robotics applications. TetraMem addresses this issue by delivering state-of-the-art in-memory computing using our proprietary non-volatile computing devices. This talk will discuss how our solution brings several orders of magnitude improvement in computing throughput and energy efficiency, ideal for those AI applications at the edge.

About the Speaker

Dr. Ge is a co-founder and the CEO of TetraMem Inc., a Silicon Valley-based startup specializing in AI accelerators using analog In-Memory Computing technology. Before that, he was a Master Technologist at HP Labs, Palo Alto. He has co-authored more than twenty publications in various journals like Scientific Research, Advance Material, Nature Electronics, Nature Materials, etc. He has more than 750 worldwide/250 US patents filings and many have been granted and used in mass production. Dr. Ge received 3 master’s and 1 Ph.D. degrees including an MBA from the Ross School of Business at the University of Michigan, an M.Sc. in IP Management from the National University of Singapore, and an M.Sc. In IC Design and a Ph.D. in Electrical Engineering from the Nanyang Technological University in Singapore.

Please register your attendance here.