PXNOR-BNN: In/With Spin-Orbit Torque MRAM Preset-XNOR Operation-Based Binary Neural Networks

Abstract

Convolution neural networks (CNNs) have demonstrated superior capability in computer vision, speech recognition, autonomous driving, and so forth, which are opening up an artificial intelligence (AI) era. However, conventional CNNs require significant matrix computation and memory usage leading to power and memory issues for mobile deployment and embedded chips. On the algorithm side, the emerging binary neural networks (BNNs) promise portable intelligence by replacing the costly massive floating-point compute-and-accumulate operations with lightweight bit-wise XNOR and popcount operations. On the hardware side, the computing-in-memory (CIM) architectures developed by the non-volatile memory (NVM) present outstanding performance regarding high speed and good power efficiency. In this paper, we propose an NVM-based CIM architecture employing a Preset-XNOR operation in/with the spin–orbit torque magnetic random access memory (SOT-MRAM) to accelerate the computation of BNNs (PXNOR-BNN). PXNOR-BNN performs the XNOR operation of BNNs inside the computing-buffer array with only slight modifications of the peripheral circuits. Based on the layer evaluation results, PXNOR-BNN can achieve similar performance compared with the read-based SOT-MRAM counterpart. Finally, the end-to-end estimation demonstrates $12.3\times $ speedup compared with the baseline with 96.6-image/s/W throughput efficiency.