  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.
