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Due to the very rapidly growing use of Artificial Neural Networks (ANNs) inreal-world applications related to machine learning and Artificial Intelligence(AI), several hardware accelerator de-signs for ANNs have been proposedrecently. In this paper, we present a novel processing-in-memory (PIM) enginecalled ODIN that employs hybrid binary-stochastic bit-parallel arithmeticin-side phase change RAM (PCRAM) to enable a low-overhead in-situ accelerationof all essential ANN functions such as multiply-accumulate (MAC), nonlinearactivation, and pooling. We mapped four ANN benchmark applications on ODIN tocompare its performance with a conventional processor-centric design and acrossbar-based in-situ ANN accelerator from prior work. The results of ouranalysis for the considered ANN topologies indicate that our ODIN acceleratorcan be at least 5.8x faster and 23.2x more energy-efficient, and up to 90.8xfaster and 1554x more energy-efficient, compared to the crossbar-based in-situANN accelerator from prior work.

ODIN: A Bit-Parallel Stochastic Arithmetic Based Accelerator for In-Situ Neural Network Processing in Phase Change RAM