Author

Suyeon Jang

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Jul 2026

ExaGEMM: Exploration Framework for CPU-Driven ML Inference via Associative In-Register Computing for Low-Bit GEMM

ExaGEMM, a workload-aware codesign and exploration framework for CPU-native low-bit GEMM via register-resident LUT execution, improves latency by 13.29x over software-only baselines, while showing that workload-aware frontier selection is especially important for mixed-precision LLM workloads.

Hyunwoo Oh, Suyeon Jang, Hanning Chen et al. · arXiv.org · 0 citations

Jul 2026

PolyQ: Codesigning End-to-End Quantization Framework for Scalable Edge CPU LLM Inference

PolyQ, a CPU-oriented compiler/quantization co-design for activation-aware channel-wise bit allocation under a user-specified average-bit budget, shows that fractional-bit CPU deployment is practical, predictable, and energy-efficient across diverse edge targets.

Hyunwoo Oh, Suyeon Jang, Hanning Chen et al. · arXiv.org · 0 citations