Compressing DMA Engine: Leveraging Activation Sparsity for Training Deep Neural Networks

3 May 2017Minsoo RhuMike O'ConnorNiladrish ChatterjeeJeff PoolStephen W. Keckler

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 work tries to address this restriction by virtualizing the memory usage of DNNs, enabling both CPU and GPU memory to be utilized for memory allocations... (read more)

PDF Abstract

Code


No code implementations yet. Submit your code now

Tasks


Results from the Paper


  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.