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.

中文翻译：

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使用SOT-MRAM多位精密纵横制阵列的自适应非均匀压缩传感

压缩感测（CS）方法用于物联网应用中利用自旋轨道扭矩磁随机存取存储器（SOT-MRAM）设备的固有计算功能，其中在传感环境下生命周期能量，设备面积和制造成本受到严格限制变化迅速。在本文中，我们提出了通过多位交叉开关阵列（ACMCA）进行自适应压缩采样的方法，以利用多位SOT-MRAM交叉开关阵列智能地生成CS测量矩阵。SPICE电路和MATLAB算法仿真结果表明，使用4位量化CS测量矩阵，ACMCA最多可将重构误差降低4dB，而生成矩阵的能耗却可以忽略不计。此外，