10 citations · 10 across the 3 of their papers we have counts for
3 papers · 1 filter
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…
CAT: Compression-Aware Training for bandwidth reduction
Chaim Baskin, Brian Chmiel, Evgenii Zheltonozhskii +3
Convolutional neural networks (CNNs) have become the dominant neural network architecture for solving visual processing tasks. One of the major obstacles hindering the ubiquitous u…
Towards Learning of Filter-Level Heterogeneous Compression of Convolutional Neural Networks
Yochai Zur, Chaim Baskin, Evgenii Zheltonozhskii +4
Recently, deep learning has become a de facto standard in machine learning with convolutional neural networks (CNNs) demonstrating spectacular success on a wide variety of tasks. H…