GDDR Memory for High-Performance AI Inference

The rapid rise in size and sophistication of AI/ML inference models requires increasingly powerful hardware deployed at the network edge and in endpoint devices. AI/ML Inference workloads for applications like edge computing and Advanced Driver Assistance Systems (ADAS) require high bandwidth memory while keeping costs low. With performance of over 20 Gbps, GDDR6 has been a good solution, providing an excellent combination of high bandwidth and cost efficiency.

As bandwidth requirements increase, the recently released GDDR7 with speeds of 36 Gbps, will provide the additional bandwidth needed moving forward for these systems. To implement high-speed memory interfaces for both the memory PHY and controller, it requires the performance and power efficiency of TSMC’s advanced process nodes. This presentation will discuss how Rambus and Cadence worked together to develop an integrated memory subsystem that is deployed widely in end-customer systems using TSMC advanced nodes. Also discussed will be the signal integrity challenges of implementing GDDR6 and GDDR7 at these high data rates.

Featured Speakers

Frank Ferro

Group Director Memory and Storage IP, Cadence Design Systems

Frank Ferro is the Group Director, Product Management at Cadence responsible for memory interface IP products. Having spent more than 20 years at AT&T, Lucent, Agere Systems, and Rambus, he has extensive experience in semiconductors and IP for datacenter (AI/ML), networking, 5G, wireless, and consumer electronics fields. Mr. Ferro holds an executive MBA from the Fuqua School of Business at Duke University, an M.S. in computer science and a B.S.E.T. in electronic engineering technology from the New Jersey Institute of Technology. 

Nidish Kamath

Director of Product Management for Memory Interface IP, Rambus

Nidish Kamath is the Director of Product Management for Memory Interface IP at Rambus. He previously held marketing and product management roles at AMD, Kioxia (formerly Toshiba Memory), Avalanche Technologies, Brocade and Qualcomm, where he worked on computational storage, SmartNICs and GPU cluster networking solutions. He has served in various standards and industry associations such as SNIA, Center for Open Source Software (CROSS), CXL Consortium, UEC and JEDEC.