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Neural networks span a wide range of applications of industrial andcommercial significance. Binary neural networks (BNN) are particularlyeffective in trading accuracy for performance, energy efficiency orhardware/software complexity. Here, we introduce a spintronic, re-configurablein-memory BNN accelerator, PIMBALL: Processing In Memory BNN AcceL(L)erator,which allows for massively parallel and energy efficient computation. PIMBALLis capable of being used as a standard spintronic memory (STT-MRAM) array and acomputational substrate simultaneously. We evaluate PIMBALL using multipleimage classifiers and a genomics kernel. Our simulation results show thatPIMBALL is more energy efficient than alternative CPU, GPU, and FPGA basedimplementations while delivering higher throughput.

PIMBALL: Binary Neural Networks in Spintronic Memory