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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Veröffentlicht in:	2025 62nd ACM/IEEE Design Automation Conference (DAC) S. 1 - 7
Hauptverfasser:	Chen, Liyan, Lyu, Dongxu, Li, Zhenyu, Jiang, Jianfei, Wang, Qin, Mao, Zhigang, Jing, Naifeng
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 22.06.2025
Schlagworte:	Computer architecture Concurrent computing Design automation Dynamic scheduling Energy consumption Kernel Large language models Layout Parallel processing Resource management
Online-Zugang:	Volltext
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