10 citations · 10 across the 3 of their papers we have counts for
3 papers
With Shared Microexponents, A Little Shifting Goes a Long Way
Bita Rouhani, Ritchie Zhao, Venmugil Elango +19
This paper introduces Block Data Representations (BDR), a framework for exploring and evaluating a wide spectrum of narrow-precision formats for deep learning. It enables compariso…
Procrustes: a Dataflow and Accelerator for Sparse Deep Neural Network Training
Dingqing Yang, Amin Ghasemazar, Xiaowei Ren +3
The success of DNN pruning has led to the development of energy-efficient inference accelerators that support pruned models with sparse weight and activation tensors. Because the m…
Full deep neural network training on a pruned weight budget
Maximilian Golub, Guy Lemieux, Mieszko Lis
We introduce a DNN training technique that learns only a fraction of the full parameter set without incurring an accuracy penalty. To do this, our algorithm constrains the total nu…