230 citations · 244 across the 3 of their papers we have counts for
4 papers · 1 filter
Accelerating Sparse Deep Neural Networks
Asit Mishra, Jorge Albericio Latorre, Jeff Pool +5
As neural network model sizes have dramatically increased, so has the interest in various techniques to reduce their parameter counts and accelerate their execution. An active area…
Structurally Sparsified Backward Propagation for Faster Long Short-Term Memory Training
Maohua Zhu, Jason Clemons, Jeff Pool +3
Exploiting sparsity enables hardware systems to run neural networks faster and more energy-efficiently. However, most prior sparsity-centric optimization techniques only accelerate…
Exploring the Regularity of Sparse Structure in Convolutional Neural Networks
Huizi Mao, Song Han, Jeff Pool +4
Sparsity helps reduce the computational complexity of deep neural networks by skipping zeros. Taking advantage of sparsity is listed as a high priority in next generation DNN accel…
Compressing DMA Engine: Leveraging Activation Sparsity for Training Deep Neural Networks
Minsoo Rhu, Mike O'Connor, Niladrish Chatterjee +2
Popular deep learning frameworks require users to fine-tune their memory usage so that the training data of a deep neural network (DNN) fits within the GPU physical memory. Prior w…