49 citations · 51 across the 4 of their papers we have counts for
3 papers · 1 filter
Robust Training of Neural Networks at Arbitrary Precision and Sparsity
Chengxi Ye, Grace Chu, Yanfeng Liu +5
The discontinuous operations inherent in quantization and sparsification introduce a long-standing obstacle to backpropagation, particularly in ultra-low precision and sparse regim…
Custom Gradient Estimators are Straight-Through Estimators in Disguise
Matt Schoenbauer, Daniele Moro, Lukasz Lew +1
Quantization-aware training comes with a fundamental challenge: the derivative of quantization functions such as rounding are zero almost everywhere and nonexistent elsewhere. Vari…
PikeLPN: Mitigating Overlooked Inefficiencies of Low-Precision Neural Networks
Marina Neseem, Conor McCullough, Randy Hsin +8
Low-precision quantization is recognized for its efficacy in neural network optimization. Our analysis reveals that non-quantized elementwise operations which are prevalent in laye…