Benchmark Comparisons of Spike-based Reconfigurable Neuroprocessor Architectures for Control Applications

被引：9

作者：

Foshie, Adam Z. ^{[1
]}

Rizzo, Charles ^{[1
]}

Das, Hritom ^{[1
]}

Zheng, ChaoHui ^{[1
]}

Plank, James S. ^{[1
]}

Rose, Garrett S. ^{[1
]}

机构：

[1] Univ Tennessee Knoxville, Min H Kao Dept Elect Engn & Comp Sci, Knoxville, TN 37916 USA

来源：

PROCEEDINGS OF THE 32ND GREAT LAKES SYMPOSIUM ON VLSI 2022, GLSVLSI 2022 | 2022年

关键词：

Neuromorphic; neuroprocessor; neuron design; refractory; training; DESIGN;

D O I：

10.1145/3526241.3530381

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Neuromorphic computing is a leading option for non von-Neumann computing architectures. With it, neural networks are developed that derive architectural inspiration from how the brain operates with neurons, synapses, and spikes. These networks are often implemented in either software or hardware based neuroprocessors designed to handle specific tasks efficiently. Even if implemented in hardware, software emulation is instrumental in determining the worthwhile features and capabilities of the architecture. In this work two novel neuroprocessors are introduced: the software-based RISP neuroprocessor, and the RAVENS hardware neuroprocessor. Several benchmark tests using control applications are performed with each neuroprocessor configured in various ways to evaluate their comparative performance and training properties.

引用

页码：383 / 386

页数：4

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