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ExaGEMM: Exploration Framework for CPU-Driven ML Inference via Associative In-Re

Low-bit GEMM is increasingly central to efficient ML inference, yet very-low-bit execution remains a poor fit for conventional CPUs. Practical deployment spans fragmented regimes-from 1/2/4-bit weights to varying activation precision-whose

https://arxiv.org/abs/2607.14622v1 ↗
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Hyunwoo Oh
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Suyeon Jang
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Hanning Chen
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Sanggeon Yun
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Ryozo Masukawa
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Mohsen Imani
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Activity & evidence

arXiv ExaGEMM: Exploration Framework for CPU-Driven ML Inference via Associative In-Register Computing for Low-Bit GEMM · based on signal ▸
arXiv PolyQ: Codesigning End-to-End Quantization Framework for Scalable Edge CPU LLM Inference · authored signal ▸

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