  Herein, a bit-wise Convolutional Neural Network (CNN) in-memory accelerator
is implemented using Spin-Orbit Torque Magnetic Random Access Memory (SOT-MRAM)
computational sub-arrays. It utilizes a novel AND-Accumulation method capable
of significantly-reduced energy consumption within convolutional layers and
performs various low bit-width CNN inference operations entirely within MRAM.
Power-intermittence resiliency is also enhanced by retaining the partial state
information needed to maintain computational forward-progress, which is
advantageous for battery-less IoT nodes. Simulation results indicate
$\sim$5.4$\times$ higher energy-efficiency and 9$\times$ speedup over
ReRAM-based acceleration, or roughly $\sim$9.7$\times$ higher energy-efficiency
and 13.5$\times$ speedup over recent CMOS-only approaches, while maintaining
inference accuracy comparable to baseline designs.
