CSrram: Area-Efficient Low-Power Ex-Situ Training Framework for Memristive Neuromorphic Circuits Based on Clustered Sparsity

Artificial Neural Networks (ANNs) play a key role in many machine learning (ML) applications but pose 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 verified on a wide range of datasets including MNIST handwritten recognition, fashion MNIST, breast cancer prediction (BCW), IRIS, and mobile health monitoring. Compared to state of the art fully connected memristive neuromorphic circuits, our CSrram with only 25% density of weights in the first junction, provides a power and area efficiency of 1.5x and 2.6x (averaged over five datasets), respectively, without any significant test accuracy loss.