Abstract
Abstract
Reservoir computing (RC) decomposes the recurrent neural network into a fixed network with recursive connections and a trainable linear network. With the advantages of low training cost and easy hardware implementation, it provides a method for the effective processing of time-domain correlation information. In this paper, we build a hardware RC system with a nonlinear MEMS resonator and build an action recognition data set with time-domain correlation. Moreover, two different universal data set are utilized to verify the classification and prediction performance of the RC hardware system. At the same time, the feasibility of the novel data set was validated by three general machine learning approaches. Specifically, the processing of this novel time-domain correlation data set obtained a relatively high success rate. These results, together with the dataset that we build, enable the broad implementation of brain-inspired computing with microfabricated devices, and shed light on the potential for the realization of integrated perception and calculation in our future work.
Funder
National Key Research and Development Program of China
Key Research Program of Frontier Science
National Natural Science Foundation of China
Reference44 articles.
1. Deep learning;LeCun;Nature,2015
2. ImageNet classification with deep convolutional neural networks;Krizhevsky;Commun. ACM,2012
3. Deep residual learning for image recognition;He,2016
4. EfficientNet: rethinking model scaling for convolutional neural networks;Tan,2019
5. A million spiking-neuron integrated circuit with a scalable communication network and interface;Merolla;Science,2014
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