Abstract

Artificial Neural Networks (ANNs) play a key role in many machine learning (ML) applications but poses arduous challenges in terms of storage and computation of network parameters. Memristive crossbar arrays (MCAs) are capable of both computation and storage, making them promising for in-memory computing enabled neural network accelerators. At the same time, the presence of a significant amount of zero weights in ANNs has motivated research in a variety of parameter reduction techniques. However, for crossbar based architectures, the study of efficient methods to take advantage of network sparsity is still in the early stage. This paper presents CSrram, an efficient ex-situ training framework for hybrid CMOS-memristive neuromorphic circuits. CSrram includes a pre-defined block diagonal clustered (BDC) sparsity algorithm to significantly reduce area and power consumption. The proposed framework is …

Date

2019

Authors

Arash Fayyazi, Souvik Kundu, Shahin Nazarian, Peter A Beerel, Massoud Pedram

Conference

2019 IEEE Computer Society Annual Symposium on VLSI (ISVLSI)

Pages

465-470

Publisher

IEEE