NVLUT
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Ablation Study of Block Size, Weight Precision, and Scale Precision in NVFP4 Inference for Low-Power Edge-Efficient Neural Networks
Announce Type: new Abstract: Energy-efficient edge inference requires reducing arithmetic cost, memory traffic, and hardware overhead. This paper presents an ablation-focused study of NVFP4 LUT-based inference for edge-efficient neural networks. The proposed NVLUT framework combines 4-bit NVFP4 activations, two-level scaling, LUT-based mantissa computation, voltage-scaled storage, and selective ECC protection.