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arXiv · 2206.14016

The Case for RISP: A Reduced Instruction Spiking Processor

James S. Plank·ChaoHui Zheng·Bryson Gullett·Nicholas Skuda·Charles Rizzo·Catherine D. Schuman·Garrett S. Rose
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Abstract

In this paper, we introduce RISP, a reduced instruction spiking processor. While most spiking neuroprocessors are based on the brain, or notions from the brain, we present the case for a spiking processor that simplifies rather than complicates. As such, it features discrete integration cycles, configurable leak, and little else. We present the computing model of RISP and highlight the benefits of its simplicity. We demonstrate how it aids in developing hand built neural networks for simple computational tasks, detail how it may be employed to simplify neural networks built with more complicated machine learning techniques, and demonstrate how it performs similarly to other spiking neurprocessors.

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James S. Plank, ChaoHui Zheng, Bryson Gullett, Nicholas Skuda, Charles Rizzo, Catherine D. Schuman, Garrett S. Rose. 2022-06-28. The Case for RISP: A Reduced Instruction Spiking Processor. https://arxiv.org/abs/2206.14016

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