197 citations · 611 across the 23 of their papers we have counts for
3 papers · 1 filter
Minimum Variance Unbiased N:M Sparsity for the Neural Gradients
Brian Chmiel, Itay Hubara, Ron Banner +1
In deep learning, fine-grained N:M sparsity reduces the data footprint and bandwidth of a General Matrix multiply (GEMM) up to x2, and doubles throughput by skipping computation of…
Energy awareness in low precision neural networks
Nurit Spingarn Eliezer, Ron Banner, Elad Hoffer +2
Power consumption is a major obstacle in the deployment of deep neural networks (DNNs) on end devices. Existing approaches for reducing power consumption rely on quite general prin…
Graph Representation Learning via Aggregation Enhancement
Maxim Fishman, Chaim Baskin, Evgenii Zheltonozhskii +3
Graph neural networks (GNNs) have become a powerful tool for processing graph-structured data but still face challenges in effectively aggregating and propagating information betwe…