Efficient optoelectronic reservoir computing with three-route input based on optical delay lines

Bao, Xiurong; Zhao, Qida; Yin, Hujun

doi:10.1364/ao.58.004111

Cited by 10 publications

(4 citation statements)

References 26 publications

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“…In modern society, with a rapidly increasing amount of information, reservoir computing (RC) has attracted more and more attention as an efficient information processing method [1,2]. RC initially evolves from two kinds of recurrent neural networks (RNNs), i.e., echo state network (ESN) and liquid state machine (LSM) [3,4]. Due to the particular network structure, RC overcomes the disadvantages of RNN, such as the complex network structure and the difficulty in training the weights of inter-layer links [5].…”

Section: Introductionmentioning

confidence: 99%

Enhanced Prediction Performance of Reservoir Computing Based on Mutually Delay-Coupled Semiconductor Lasers via Parameter Mismatch

et al. 2022

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As an efficient information processing method, reservoir computing (RC) is essential to artificial neural networks (ANNs). Via the Santa Fe time series prediction task, we numerically investigated the effect of the mismatch of some critical parameters on the prediction performance of the RC based on two mutually delay-coupled semiconductor lasers (SLs) with optical injection. The results show that better prediction performance can be realized by setting appropriate parameter mismatch scenarios. Especially for the situation with large prediction errors encountered in the RC with identical laser parameters, a suitable parameter mismatch setting can achieve computing performance improvement of an order of magnitude. Our research is instructive for the hardware implementation of laser-based RC, where the parameter mismatch is unavoidable.

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Section: Introductionmentioning

confidence: 99%

Enhanced Prediction Performance of Reservoir Computing Based on Mutually Delay-Coupled Semiconductor Lasers via Parameter Mismatch

et al. 2022

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“…Here, we add to the exciting progress made in the RC field and experimentally demonstrate a fully-packaged opto-electronic RC system consisting of a Mach-Zehnder electro-optic modulator (EOM) and a FPGA circuit. In our design, both the delay line and filters are implemented digitally within FPGA, which renders the whole system compact and immune to optical drifts and noise, especially compared to fiber-optical realizations [42][43][44]. Leveraging the filters inside the FPGA, we are able to achieve more dynamics and connections between reservoir nodes.…”

Section: Introductionmentioning

confidence: 99%

Efficient Reservoir Computing using Field Programmable Gate Array and Electro-optic Modulation

Kumar

Jin

et al. 2021

Preprint

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We experimentally demonstrate a hybrid reservoir computing system consisting of an electro-optic modulator and field programmable gate array (FPGA). It implements delay lines and filters digitally for flexible dynamics and high connectivity, while supporting a large number of reservoir nodes. To evaluate the system's performance and versatility, three benchmark tests are performed. The first is the 10 𝒕𝒉 order Nonlinear Auto-Regressive Moving Average test (NARMA-10), where the predictions of 1000 and 25,000 steps yield impressively low normalized root mean square errors (NRMSE's) of 0.142 and 0.148, respectively. Such accurate predictions over into the far future speak to its capability of large sample size processing, as enabled by the present hybrid design. The second is the Santa Fe laser data prediction, where a normalized mean square error (NMSE) of 6.73×10 −3 is demonstrated. The third is the isolate spoken digit recognition, with a word error rate close to 0.34%. Accurate, versatile, flexibly reconfigurable, and capable of long-term prediction, this reservoir computing system could find a wealth of impactful applications in real-time information processing, weather forecasting, and financial analysis.

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“…The parallel information processing of this RC system is implemented based on the dynamical responses of the X polarization component and Y polarization component in the VCSEL [ 17 ]. Some parallel RC systems with multiple feedback loops attached to a single nonlinear node are also designed, which can process two or three route independent signals simultaneously [ 18 , 19 ]. An all-optical reservoir composed of a semiconductor laser with two optical feedbacks was proposed that successfully realized the simultaneous recognition of three route signals [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

“…Some parallel RC systems with multiple feedback loops attached to a single nonlinear node are also designed, which can process two or three route independent signals simultaneously [ 18 , 19 ]. An all-optical reservoir composed of a semiconductor laser with two optical feedbacks was proposed that successfully realized the simultaneous recognition of three route signals [ 18 ]. The simultaneous recognition of two routes of independent optical packet headers is implemented by an all-optical RC utilizing a semiconductor ring laser with double optical feedbacks [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

A Multiple-Input Multiple-Output Reservoir Computing System Subject to Optoelectronic Feedbacks and Mutual Coupling

Bao

Zhao

Yin

2020

Entropy

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In this paper, a multiple-input multiple-output reservoir computing (RC) system is proposed, which is composed of multiple nonlinear nodes (Mach–Zehnder modulators) and multiple mutual-coupling loops of optoelectronic delay lines. Each input signal is added into every mutual-coupling loop to implement the simultaneous recognition of multiple route signals, which results in the signal processing speed improving and the number of routes increasing. As an example, the four-route input and four-route output RC is simultaneously realized by numerical simulations. The results show that this type of RC system can successfully recognize the four-route optical packet headers with 3-bit, 8-bit, 16-bit, and 32-bit, and four-route independent digital speeches. When the white noise is added to the signals such that the signal-to-noise ratio (SNR) of the optical packet headers and the digital speeches are 35 dB and 20 dB respectively, the normalized root mean square errors (NRMSEs) of the signal recognition are all close to 0.1. The word error rates (WERs) of the optical packet header recognition are 0%. The WER of the digital speech recognition is 1.6%. The eight-route input and eight-route output RC is also numerically simulated. The recognition of the eight-route 3-bit optical packet headers is implemented. The parallel processing of multiple-route signals and the high recognition accuracy are implemented by this proposed system.

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Efficient optoelectronic reservoir computing with three-route input based on optical delay lines

Cited by 10 publications

References 26 publications

Enhanced Prediction Performance of Reservoir Computing Based on Mutually Delay-Coupled Semiconductor Lasers via Parameter Mismatch

Enhanced Prediction Performance of Reservoir Computing Based on Mutually Delay-Coupled Semiconductor Lasers via Parameter Mismatch

Efficient Reservoir Computing using Field Programmable Gate Array and Electro-optic Modulation

A Multiple-Input Multiple-Output Reservoir Computing System Subject to Optoelectronic Feedbacks and Mutual Coupling

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