VQV Signal
CHIPSMORE is a multi-mode, multi-request LLM inference accelerator that combines compute-in-interconnect and compute-in-memory techniques to address variability in adaptation modes, context lengths, and request concurrency. It aims to improve utilization, memory efficiency, and scalable performance...
CHIPSMORE is a multi-mode, multi-request LLM inference accelerator that combines compute-in-interconnect and compute-in-memory techniques to address variability in adaptation modes, context lengths, and request concurrency. It aims to improve utilization, memory efficiency, and scalable performance...
AI-assisted summary based on the listed source.
Large language model (LLM) inference exhibits substantial variability across adaptation modes, context lengths, and request concurrency, creating challenges for maintaining high utilization, memory efficiency, and scalable performance on compute-in-memory (CIM) accelerators. This paper presents CHIPSMORE, a...
LLM inference faces challenges in maintaining efficiency and scalability due to diverse workload characteristics. CHIPSMORE's integrated approach targets these issues to enhance accelerator performance for large language models.
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VQV surfaced this signal because it is recent, relevant to LLM Inference, connected to arXiv.
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