Toward Energy Efficient STT-MRAM-based Near Memory Computing Architecture for Embedded Systems

Abstract

Convolutional Neural Networks (CNNs) have significantly impacted embedded system applications across various domains. However, this exacerbates the real-time processing and hardware resource-constrained challenges of embedded systems. To tackle these issues, we propose spin-transfer torque magnetic random-access memory (STT-MRAM)-based near memory computing (NMC) design for embedded systems. We optimize this design from three aspects: Fast-pipelined STT-MRAM readout scheme provides higher memory bandwidth for NMC design, enhancing real-time processing capability with a non-trivial area overhead. Direct index compression format in conjunction with digital sparse matrix-vector multiplication (SpMV) accelerator supports various matrices of practical applications that alleviate computing resource requirements. Custom NMC instructions and stream converter for NMC systems dynamically adjust available hardware resources for better utilization. Experimental results demonstrate that the memory bandwidth of STT-MRAM achieves 26.7GB/s. Energy consumption and latency improvement of digital SpMV accelerator are up to 64x and 1120x across sparsity matrices spanning from 10% to 99.8%. Single-precision and double-precision elements transmission increased up to 8x and 9.6x, respectively. Furthermore, our design achieves a throughput of up to 15.9x over state-of-the-art designs.