Neuromorphic Computing for Temporal Scientific Data Classification

被引：6

作者：

Schuman, Catherine D. ^{[1
]}

Potok, Thomas E. ^{[1
]}

Young, Steven ^{[1
]}

Patton, Robert ^{[1
]}

Perdue, Gabriel ^{[2
]}

Chakma, Gangotree ^{[3
]}

Wyer, Austin ^{[3
]}

Rose, Garrett S. ^{[3
]}

机构：

[1] Oak Ridge Natl Lab, Oak Ridge, TN 37830 USA

[2] Fermilab Natl Accelerator Lab, POB 500, Batavia, IL 60510 USA

[3] Univ Tennessee, Knoxville, TN USA

来源：

PROCEEDINGS OF NEUROMORPHIC COMPUTING SYMPOSIUM (NCS 2017) | 2017年

基金：

美国国家科学基金会;

关键词：

SPIKING NEURAL-NETWORKS; DESIGN; FPGA;

D O I：

10.1145/3183584.3183612

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

In this work, we apply a spiking neural network model and an associated memristive neuromorphic implementation to an application in classifying temporal scientific data. We demonstrate that the spiking neural network model achieves comparable results to a previously reported convolutional neural network model, with significantly fewer neurons and synapses required.

引用

页数：6

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