The discussion also touches on the evolution of memory architectures with the adoption of technologies like CXL and HBM. The experts suggest that these technologies could provide new optimization opportunities for computer architects and allow for a mix of different memory technologies for different levels of cache. They also discuss the impact of large language models migrating to the edge and the resulting effect on memory management. The experts conclude that SRAM helps balance low power and high performance in AI and other systems by allowing for quick data retrieval and reducing the need to go off-chip.
Key takeaways:
- SRAM is a key component in AI processing solutions due to its compatibility with CMOS logic process, high performance, and instant data accessibility. It is particularly crucial in applications that require substantial memory next to the processing element.
- The amount of SRAM needed varies depending on the application, such as data center versus device, or training versus inference. The need for SRAM is a constant, but the amount and placement become an architectural tradeoff question based on the specifics of the application.
- New technologies like CXL and HBM offer new optimization opportunities for computer architects, potentially allowing for a mix of different memory technologies for different levels of cache. This could lead to more customized hardware solutions without requiring a complete new design from scratch.
- The scaling difference between logic and SRAM as technology advances could impact decisions about memory management, power, and manufacturability. This could lead to changes in the amount of resources in the overall system at the local compute engine level, potentially requiring a shift from SRAM to traditional flop-based designs.