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