Adaptive Non-Uniform Compressive Sensing Using SOT-MRAM Multi-Bit Precision Crossbar Arrays

Abstract

A Compressive Sensing (CS) approach is applied to utilize intrinsic computation capabilities of Spin-Orbit Torque Magnetic Random Access Memory (SOT-MRAM) devices for IoT applications wherein lifetime energy, device area, and manufacturing costs are highly-constrained while the sensing environment varies rapidly. In this manuscript, we propose the Adaptive Compressed-sampling via Multi-bit Crossbar Array (ACMCA) approach to intelligently generate the CS measurement matrix using a multi-bit SOT-MRAM crossbar array. SPICE circuit and MATLAB algorithm simulation results indicate that ACMCA reduces reconstruction error by up to 4dB using a 4-bit quantized CS measurement matrix while incurring a negligible increase in the energy consumption of generating the matrix. Additionally, we introduce an algorithm called Energy-aware Adaptive Sensing for IoT (EASI) which determines the frequency of measurement matrix updates within the energy budget of an IoT device.