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cs.LG2026
Entropy-Constrained Adaptive Stochastic Quantization
Ran Ben Basat, Yaniv Ben-Itzhak, Michael Mitzenmacher +1
Adaptive stochastic quantization (ASQ) is a recently introduced quantization approach that optimizes the Mean Squared Error (MSE) for a given input while preserving unbiasedness. I…
cs.LG2024
Beyond Throughput and Compression Ratios: Towards High End-to-end Utility of Gradient Compression
Wenchen Han, Shay Vargaftik, Michael Mitzenmacher +2
Gradient aggregation has long been identified as a major bottleneck in today's large-scale distributed machine learning training systems. One promising solution to mitigate such bo…
cs.LG2024
THC: Accelerating Distributed Deep Learning Using Tensor Homomorphic Compression
Minghao Li, Ran Ben Basat, Shay Vargaftik +4
Deep neural networks (DNNs) are the de facto standard for essential use cases, such as image classification, computer vision, and natural language processing. As DNNs and datasets…