Journal
APPLIED SCIENCES-BASEL
Volume 11, Issue 9, Pages -Publisher
MDPI
DOI: 10.3390/app11094232
Keywords
VCSEL; semiconductor lasers; nonlinear dynamics; delay systems; machine learning; neuromorphic computing; reservoir computing
Categories
Funding
- Research Foundation Flanders (FWO) [G028618N, G029519N, G006020N]
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Delay-based reservoir computing using spin-VCSELs allows high-speed computing. The delay time and feedback rate significantly influence the nonlinear dynamics at short time scales. The system shows superior performance in time series prediction tasks.
Delay-based reservoir computing (RC), a neuromorphic computing technique, has gathered lots of interest, as it promises compact and high-speed RC implementations. To further boost the computing speeds, we introduce and study an RC setup based on spin-VCSELs, thereby exploiting the high polarization modulation speed inherent to these lasers. Based on numerical simulations, we benchmarked this setup against state-of-the-art delay-based RC systems and its parameter space was analyzed for optimal performance. The high modulation speed enabled us to have more virtual nodes in a shorter time interval. However, we found that at these short time scales, the delay time and feedback rate heavily influence the nonlinear dynamics. Therefore, and contrary to other laser-based RC systems, the delay time has to be optimized in order to obtain good RC performances. We achieved state-of-the-art performances on a benchmark timeseries prediction task. This spin-VCSEL-based RC system shows a ten-fold improvement in processing speed, which can further be enhanced in a straightforward way by increasing the birefringence of the VCSEL chip.
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