Recom: An efficient resistive accelerator for compressed deep neural networks
Deep Neural Networks (DNNs) play a key role in prevailing machine learning applications. Resistive random-Access memory (ReRAM) is capable of both computation and storage, contributing to the acceleration on DNNs by processing in memory. Besides, a significant amount of zero weights is observed in DNNs, providing a space to reduce computation cost further by skipping ineffectual calculations associated with them. However, the irregular distribution of zero weights in DNNs makes it difficult for resistive accelerators to take advantage of the sparsity as expected efficiently, because of its high reliance on regular matrix-vector multiplication in ReRAM. In this work, we propose ReCom, the first resistive accelerator to support sparse DNN processing. ReCom is an efficient resistive accelerator for compressed deep neural networks, where DNN weights are structurally compressed to eliminate zero parameters and become hardware-friendly. Zero DNN activation is also considered at the same time. Two technologies, Structurally-compressed Weight Oriented Fetching (SWOF) and In-layer Pipeline for Memory and Computation (IPMC), are particularly proposed. In our evaluation, ReCom can achieve 3.37x speedup and 2.41x energy efficiency compared to a state-of-The-Art resistive accelerator.
