RED-PIM: Reducing Data Movement for Transformers using Processing-in-Memory

arXiv cs.LG5d4 min read

arXiv:2607.21731v1 Announce Type: new Abstract: Transformers are widely used across many domains, including natural language processing, computer vision, web search, and DNA sequence analysis. Given their broad applicability, improving the performance of transformer models is critical. However, the high volume of data movement between processing units and memory during attention operations significantly limits their efficiency. Processing-In-Memory (PIM) mitigates this issue by performing computations directly inside memory. While prior work has proposed PIM-based transformer implementations,

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RED-PIM: Reducing Data Movement for Transformers using Processing-in-Memory

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