AttenPIM: Accelerating LLM Attention with Dual-mode GEMV in Processing-in-Memory

Large Language Models (LLMs) have demonstrated unprecedented generative performance across a wide range of applications. While recent heterogeneous architectures attempt to address the memory-bound bottleneck from attention computations by processing-in-memory (PIM) offloading, they overlook two cri...

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Vydané v:	2025 62nd ACM/IEEE Design Automation Conference (DAC) s. 1 - 7
Hlavní autori:	Chen, Liyan, Lyu, Dongxu, Li, Zhenyu, Jiang, Jianfei, Wang, Qin, Mao, Zhigang, Jing, Naifeng
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 22.06.2025
Predmet:	Computer architecture Concurrent computing Design automation Dynamic scheduling Energy consumption Kernel Large language models Layout Parallel processing Resource management
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