Industry Expert Blogs

To coincide with the launch of the industry’s first HBM4 Controller IP from Rambus, we talked to Nidish Kamath, Director of Product Management for Memory Interface IP.

The discussion highlighted how AI applications are driving the increased demand for HBM-based systems; the transition to Generative AI applications has led to significant performance and efficiency demands on the underlying compute infrastructure. The HBM4 standard, currently under development by JEDEC, will introduce new features designed to support the future memory requirements of AI applications.

Rambus is supporting designers with the transition to a new generation of HBM designs with an innovative digital controller that manages some of the implementation challenges that emerge when designing at higher data rates.

Check out the full video interview below or skip to read the key takeaways.

Expert

• Nidish Kamath, Director of Product Management, Rambus

Key Takeaways

1. AI Drives HBM Evolution: The rapid evolution of the HBM specification is driven by the increasing demands of AI applications as they evolve from machine learning to more generalized and widely deployed AI. These applications pose critical performance and efficiency challenges for the underlying compute infrastructure.
2. HBM4 Standard Development: The HBM4 standard, currently under development by JEDEC, is set to introduce a doubled channel count per stack compared to HBM3, with a larger physical footprint. HBM4 will support speeds of 6.4 Gigabits per second (Gbps) with ongoing discussions regarding support for higher data rates.
3. HBM4 Implementation Challenges: HBM4 will specify 24 and 32 Gigabit capacities, with options for supporting 4-, 8-, and 16-high TSV stacks. The increased channel count introduces implementation challenges such as packaging complexities, increased power density, as well as thermal and DRAM refresh management challenges.
4. Rambus HBM4 Controller Solution: The Rambus HBM4 Controller IP is designed to manage the complexity of data parallelism at higher speeds. For example, it has a re-ordering logic that optimizes the outgoing HBM transactions and incoming HBM read data to keep the high bandwidth data interface efficiently utilized for the given performance and power target.
5. Rambus HBM Expertise and Partnerships: The Rambus Memory Controller engineering team has over a decade of specialized expertise in designing high performance memory interface IP, including over 150 design wins for HBM and GDDR. The team works closely with PHY memory vendors to ensure any new PHY releases are fully tested out and supported for end customers.

Key Quote

Today’s AI applications pose critical performance and efficiency challenges for the underlying compute infrastructure. We are seeing widespread use of GPUs and AI accelerators that need to evolve quickly to meet the demanding performance requirements of these applications. This is one of the key reasons why we are seeing HBM4-based system development proceed at a more rapid pace compared to previous generations of the standard.