Multi-bit MRAM based high performance neuromorphic accelerator for image classification

Abstract

Binary neural networks (BNNs) are the most efficient solution to bridge the design gap of the hardware implementation of neural networks in a resource-constrained environment. Spintronics is a prominent technology among emerging fields for next-generation on-chip non-volatile memory. Spin transfer torque (STT) and spin-orbit torque (SOT) based magnetic random-access memory (MRAM) offer non-volatility and negligible static power. Over the last few years, STT and SOT-based multilevel spintronic memories have emerged as a promising solution to attain high storage density. This paper presents the operation principle and performance evaluation of spintronics-based single-bit STT and SOT MRAM, dual-level cells, three-level cells (TLCs), and four-level cells. Further, multi-layer perceptron architectures have been utilized to perform MNIST image classification with these multilevel devices. The performance of the complete system level consisting of crossbar arrays with various MRAM bit cells in terms of area, energy, and latency is evaluated. The throughput efficiency of the BNN accelerator using TLCs is 26.6X, and 3.61X higher than conventional single-bit STT-MRAM, and SOT-MRAM respectively.

Keywords

• non-volatile memory (NVM)
• Magnetic random-access memory (MRAM)
• Binary neural network (BNN)
• Hardware accelerator