arXiv Machine Learning June 9, 2026 By Ovishake Sen, Venkata Nithin Kamineni, Daniel Lobo, Swarup Bhunia, Rickard Ewetz, Baibhab Chatterjee

Characterizing the Impact of NVFP4 Quantization for Low-Power Edge AI Deployment

Read the original on arXiv Machine Learning →

arXiv:2606. 06527v2 Announce Type: replace-cross Abstract: Energy-efficient neural-network inference at the edge requires reducing arithmetic cost, memory traffic, computation energy, and storage overhead while maintaining acceptable accuracy.

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