%AAngizi, Shaahin%AHe, Zhezhi%AParveen, Farhana%AFan, Deliang%D2018%I %K %MOSTI ID: 10059775 %PMedium: X %TIMCE: Energy-efficient bit-wise in-memory convolution engine for deep neural network %XIn this paper, we pave a novel way towards the concept of bit-wise In-Memory Convolution Engine (IMCE) that could implement the dominant convolution computation of Deep Convolutional Neural Networks (CNN) within memory. IMCE employs parallel computational memory sub-array as a fundamental unit based on our proposed Spin Orbit Torque Magnetic Random Access Memory (SOT-MRAM) design. Then, we propose an accelerator system architecture based on IMCE to efficiently process low bit-width CNNs. This architecture can be leveraged to greatly reduce energy consumption dealing with convolutional layers and also accelerate CNN inference. The device to architecture co-simulation results show that the proposed system architecture can process low bit-width AlexNet on ImageNet data-set favorably with 785.25μJ/img, which consumes ~3× less energy than that of recent RRAM based counterpart. Besides, the chip area is ~4× smaller. Country unknown/Code not availablehttps://doi.org/10.1109/ASPDAC.2018.8297291OSTI-MSA