8.4 Gbps HBM3-ready memory subsystem for AI/ML

September 01, 2021 // By Jean-Pierre Joosting
8.4 Gbps HBM3-ready memory subsystem for AI/ML
Provides a fully-integrated PHY and digital controller and enables terabyte-scale bandwidth accelerators for artificial intelligence, machine learning and high-performance computing applications

Rambus has announced the Rambus HBM3-ready memory interface subsystem consisting of a fully-integrated PHY and digital controller. Supporting breakthrough data rates of up to 8.4 Gbps, the memory subsystem can deliver over a terabyte per second of bandwidth, more than double that of high-end HBM2E memory subsystems. With a market-leading position in HBM2/2E memory interface deployments, Rambus is ideally suited to enable customers to implement accelerators using next-generation HBM3 memory for artificial intelligence (AI), machine learning (ML) and high-performance computing (HPC) applications.

"The memory bandwidth requirements of AI/ML training are insatiable with leading-edge training models now surpassing billions of parameters," said Soo Kyoum Kim, associate vice president, Memory Semiconductors at IDC. "The Rambus HBM3-ready memory subsystem raises the bar for performance enabling state-of-the-art AI/ML and HPC applications."

Rambus achieves HBM3 operation of up to 8.4 Gbps leveraging over 30 years of high-speed signaling expertise, and a strong history of 2.5D memory system architecture design and enablement. In addition to the fully-integrated HBM3-ready memory subsystem, Rambus provides its customers with interposer and package reference designs to speed their products to market.

"With the performance achieved by our HBM3-ready memory subsystem, designers can deliver the bandwidth needed by the most demanding designs," said Matt Jones, general manager of Interface IP at Rambus. "Our fully-integrated PHY and digital controller builds on our broad installed base of HBM2 customer deployments and is backed by a full suite of support services to ensure first-time right implementations for mission-critical AI/ML designs."


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