197 citations · 611 across the 23 of their papers we have counts for
4 papers · 1 filter
Loss Aware Post-training Quantization
Yury Nahshan, Brian Chmiel, Chaim Baskin +4
Neural network quantization enables the deployment of large models on resource-constrained devices. Current post-training quantization methods fall short in terms of accuracy for I…
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…
Thanks for Nothing: Predicting Zero-Valued Activations with Lightweight Convolutional Neural Networks
Gil Shomron, Ron Banner, Moran Shkolnik +1
Convolutional neural networks (CNNs) introduce state-of-the-art results for various tasks with the price of high computational demands. Inspired by the observation that spatial cor…
Feature Map Transform Coding for Energy-Efficient CNN Inference
Brian Chmiel, Chaim Baskin, Ron Banner +5
Convolutional neural networks (CNNs) achieve state-of-the-art accuracy in a variety of tasks in computer vision and beyond. One of the major obstacles hindering the ubiquitous use…