End-to-End Transformer Acceleration Through Processing-in-Memory Architectures (arxiv.org)

Recent advancements in processing-in-memory architectures have introduced a groundbreaking method for accelerating transformer models, which are pivotal in natural language processing and large language models (LLMs). Traditional implementations of transformers face significant challenges, including high latency and energy consumption stemming from the attention mechanism's dependence on extensive matrix multiplications and frequent data transfer between memory and compute units. Additionally, issues like the unpredictably expanding key-value cache and the quadratic complexity related to sequence length present significant bottlenecks during large-scale inference. The proposed solutions aim to fundamentally reshape how attention and feed-forward computations are executed, significantly reducing off-chip data transfers while dynamically managing memory growth through compression and pruning techniques. By reinterpreting attention as an associative memory operation, these methods decrease both complexity and hardware resource usage. Comparisons to state-of-the-art accelerators and general-purpose GPUs reveal remarkable improvements in energy efficiency and latency, suggesting that this approach could revolutionize transformer deployments, making them more scalable and efficient than ever before. This innovation stands to greatly impact the AI/ML community by enhancing the feasibility and performance of large models in real-world applications.