2026
LogART: Pushing the Limit of Efficient Logarithmic Post-Training Quantization
ICLR 2026poster
Efficient deployment of deep neural networks increasingly relies on Post-Training Quantization (PTQ). Logarithmic PTQ, in particular, promises multiplier-free hardware efficiency, but its performance is often limited by the nonlinear and symmetric quantization grid and standard rounding-to-nearest (…