ReBNN: in-situ acceleration of binarized neural networks in ReRAM using complementary resistive cell

TitleReBNN: in-situ acceleration of binarized neural networks in ReRAM using complementary resistive cell
Publication TypeJournal Article
Year of Publication2019
AuthorsL Song, Y Wu, X Qian, H Li, and Y Chen
JournalCcf Transactions on High Performance Computing
Volume1
Start Page196
Issue3-4
Pagination196 - 208
Date Published12/2019
Abstract

Resistive random access memory (ReRAM) has been proven capable to efficiently perform in-situ matrix-vector computations in convolutional neural network (CNN) processing. The computations are often conducted on multi-level cell (MLC) that have limited precision and hence, show significant vulnerability to noises. The binarized neural network (BNN) is a hardware-friendly model that can dramatically reduce the computation and storage overheads. However, XNOR, which is the key operation in BNNs, cannot be directly computed in-situ in ReRAM because of its nonlinear behavior. To enable real in-situ processing of BNNs in ReRAM, we modified the BNN algorithm to enable direct computation of XNOR, POPCOUNT and POOL based on ReRAM cells. We also proposed the complementary resistive cell (CRC) design to efficiently conduct XNOR operations and optimized the pipeline design with decoupled buffer and computation stages. Our results show our scheme, namely, ReBNN, improves the system performance by 25.36 × and the energy efficiency by 4.26 × compared to conventional ReRAM based accelerator, and ensures a throughput higher than state-of-the-art BNN accelerators. The correctness of the modified algorithm is also validated.

DOI10.1007/s42514-019-00014-8
Short TitleCcf Transactions on High Performance Computing