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arXiv · 2006.03007

An Inference and Learning Engine for Spiking Neural Networks in Computational RAM (CRAM)

Hüsrev Cılasun·Salonik Resch·Zamshed I. Chowdhury·Erin Olson·Masoud Zabihi·Zhengyang Zhao·Thomas Peterson·Keshab Parhi·Jian-Ping Wang·Sachin S. Sapatnekar·Ulya Karpuzcu
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Abstract

Spiking Neural Networks (SNN) represent a biologically inspired computation model capable of emulating neural computation in human brain and brain-like structures. The main promise is very low energy consumption. Unfortunately, classic Von Neumann architecture based SNN accelerators often fail to address demanding computation and data transfer requirements efficiently at scale. In this work, we propose a promising alternative, an in-memory SNN accelerator based on Spintronic Computational RAM (CRAM) to overcome scalability limitations, which can reduce the energy consumption by up to 164.1$\times$ when compared to a representative ASIC solution.

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Hüsrev Cılasun, Salonik Resch, Zamshed I. Chowdhury, Erin Olson, Masoud Zabihi, Zhengyang Zhao, Thomas Peterson, Keshab Parhi, Jian-Ping Wang, Sachin S. Sapatnekar, Ulya Karpuzcu. 2020-06-04. An Inference and Learning Engine for Spiking Neural Networks in Computational RAM (CRAM). https://doi.org/10.1145/3475963

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