117 citations · 133 across the 4 of their papers we have counts for
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cs.AR2025★ 6 cited
Pimba: A Processing-in-Memory Acceleration for Post-Transformer Large Language Model Serving
Wonung Kim, Yubin Lee, Yoonsung Kim +8
Transformers are the driving force behind today's Large Language Models (LLMs), serving as the foundation for their performance and versatility. Yet, their compute and memory costs…
cs.AR2024★ 117 cited
NeuPIMs: NPU-PIM Heterogeneous Acceleration for Batched LLM Inferencing
Guseul Heo, Sangyeop Lee, Jaehong Cho +6
Modern transformer-based Large Language Models (LLMs) are constructed with a series of decoder blocks. Each block comprises three key components: (1) QKV generation, (2) multi-head…