197 citations · 611 across the 23 of their papers we have counts for
3 papers · 1 filter
Improving Post Training Neural Quantization: Layer-wise Calibration and Integer Programming
Itay Hubara, Yury Nahshan, Yair Hanani +2
Lately, post-training quantization methods have gained considerable attention, as they are simple to use, and require only a small unlabeled calibration set. This small dataset can…
Neural gradients are near-lognormal: improved quantized and sparse training
Brian Chmiel, Liad Ben-Uri, Moran Shkolnik +3
While training can mostly be accelerated by reducing the time needed to propagate neural gradients back throughout the model, most previous works focus on the quantization/pruning…
Robust Quantization: One Model to Rule Them All
Moran Shkolnik, Brian Chmiel, Ron Banner +4
Neural network quantization methods often involve simulating the quantization process during training, making the trained model highly dependent on the target bit-width and precise…