Adaptive Non-Uniform Compressive Sensing using SOT-MRAM Multibit Crossbar Arrays

TL;DR

This paper introduces an adaptive compressive sensing method leveraging multibit SOT-MRAM crossbar arrays to improve IoT sensing efficiency, reducing error and area while maintaining low energy use.

Contribution

It presents the ACMCA approach that intelligently generates CS measurement matrices using multibit SOT-MRAM arrays, enhancing performance and reducing device area.

Findings

Reduces TNMSE by 5dB on average.

Achieves up to 160μm² area reduction.

Maintains low energy consumption.

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 Multibit Crossbar Array (ACMCA) approach to intelligently generate the CS measurement matrix using a multibit SOT-MRAM crossbar array. SPICE circuit and MATLAB algorithm simulation results indicate that ACMCA reduces reconstruction Time-Averaged Normalized Mean Squared Error (TNMSE) by 5dB on average while providing up to 160$\mu$m$^2$ area reduction compared to a similar previous design presented in the literature while incurring a negligible increase in the energy consumption of generating the CS measurement matrix.