A recent technical paper titled “WWW: What, When, Where to Compute-in-Memory” by researchers at Purdue University delves into the potential benefits of Compute-in-Memory (CiM) for improving energy efficiency and performance in machine learning (ML) inference.
CiM has emerged as a promising solution to reduce data movement costs in von Neumann machines. It enables parallel matrix multiplication operations within the memory itself, which is crucial for ML inference tasks. However, integrating CiM raises important questions about the type of CiM to use, determining when to utilize CiM, and deciding where to integrate it within the memory hierarchy.
The researchers employed Timeloop-Accelergy for early system-level evaluations of various CiM prototypes, both analog and digital primitives. They integrated CiM into different cache memory levels in a baseline architecture similar to Nvidia A100 and tailored the dataflow for different ML workloads.
The experiments conducted in this work showcase the significant benefits of CiM architectures. With INT-8 precision, the proposed CiM architectures achieved up to 0.12x lower energy consumption compared to the established baseline. Moreover, through techniques like weight interleaving and duplication, they observed up to 4x performance gains.
The findings of this research provide valuable insights into the optimal usage of CiM for ML inference acceleration. It sheds light on the appropriate type of CiM to choose, the circumstances in which CiM outperforms standard processing cores, and the ideal integration points within the cache hierarchy for GEMM acceleration.
By exploring CiM integration for ML inference, this paper contributes to the ongoing efforts in increasing the energy efficiency of artificial intelligence systems. As ML workloads continue to grow in scale and complexity, CiM presents a potential breakthrough that could help meet these challenges while staying within fixed power budgets.
Overall, this research highlights the potential of CiM to revolutionize ML inference acceleration and provides a foundation for future advancements in this domain. Further exploration and development in CiM technologies could pave the way for more energy-efficient and high-performance computing systems.
The source of the article is from the blog shakirabrasil.info