ODIN: A Bit-Parallel Stochastic Arithmetic Based Accelerator for In-Situ Neural Network Processing in Phase Change RAM
This work addresses hardware acceleration for ANNs in machine learning and AI applications, offering significant performance and energy improvements over existing methods, though it is incremental as it builds on prior processing-in-memory designs.
The paper tackles the problem of accelerating artificial neural networks (ANNs) by proposing ODIN, a processing-in-memory engine using bit-parallel stochastic arithmetic in phase change RAM, which achieves speedups of 5.8x to 90.8x and energy efficiency gains of 23.2x to 1554x compared to prior crossbar-based accelerators.
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.