arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2102.09722physics.opticscs.ET
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基于空间光调制器的全光神经网络的可扩展性

Scalability of all-optical neural networks based on spatial light modulators

Ying Zuo, Zhao Yujun, You-Chiuan Chen, Shengwang Du, Junwei Liu

摘要:

人工神经网络的光学实现由于其在光速并行计算方面的潜力而引起了人们的极大关注。虽然最近已经通过实验证明了具有少数神经元的全光深度神经网络(AODNs)具有可接受的误差,但大规模AODNs的可行性仍然未知,因为随着神经元和连接数量的增加,误差可能不可避免地积累。在这里,我们展示了一个可扩展的AODNN与可编程的线性操作和可调的非线性激活函数。我们通过测量和分析从单个神经元传播到整个网络的错误来验证其可扩展性。通过对手写体数字和手写体样式的识别,进一步验证了AODNN的可行性。

英文摘要:

Optical implementation of artificial neural networks has been attracting great attention due to its potential in parallel computation at speed of light. Although all-optical deep neural networks (AODNNs) with a few neurons have been experimentally demonstrated with acceptable errors recently, the feasibility of large scale AODNNs remains unknown because error might accumulate inevitably with increasing number of neurons and connections. Here, we demonstrate a scalable AODNN with programmable linear operations and tunable nonlinear activation functions. We verify its scalability by measuring and analyzing errors propagating from a single neuron to the entire network. The feasibility of AODNNs is further confirmed by recognizing handwritten digits and fashions respectively.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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