Author:
Borghi Massimo,Biasi Stefano,Pavesi Lorenzo
Abstract
AbstractPhotonic implementations of reservoir computing (RC) promise to reach ultra-high bandwidth of operation with moderate training efforts. Several optoelectronic demonstrations reported state of the art performances for hard tasks as speech recognition, object classification and time series prediction. Scaling these systems in space and time faces challenges in control complexity, size and power demand, which can be relieved by integrated optical solutions. Silicon photonics can be the disruptive technology to achieve this goal. However, the experimental demonstrations have been so far focused on spatially distributed reservoirs, where the massive use of splitters/combiners and the interconnection loss limits the number of nodes. Here, we propose and validate an all optical RC scheme based on a silicon microring (MR) and time multiplexing. The input layer is encoded in the intensity of a pump beam, which is nonlinearly transferred to the free carrier concentration in the MR and imprinted on a secondary probe. We harness the free carrier dynamics to create a chain-like reservoir topology with 50 virtual nodes. We give proof of concept demonstrations of RC by solving two nontrivial tasks: the delayed XOR and the classification of Iris flowers. This forms the basic building block from which larger hybrid spatio-temporal reservoirs with thousands of nodes can be realized with a limited set of resources.
Publisher
Springer Science and Business Media LLC
Reference48 articles.
1. Russakovsky, O. et al. Imagenet large scale visual recognition challenge. Int. J. Comput. Vis 115, 211–252 (2015).
2. Buetti-Dinh, A. et al. Deep neural networks outperform human experts capacity in characterizing bioleaching bacterial biofilm composition. Biotechnol. Rep. 22, e00321 (2019).
3. Silver, D. et al. Mastering the game of go with deep neural networks and tree search. Nature 529, 484–489 (2016).
4. Assael, Y. M., Shillingford, B., Whiteson, S. & De Freitas, N. Lipnet: End-to-end sentence-level lipreading. arXiv preprint: arXiv:1611.01599 (2016).
5. Jaeger, H. The echo state approach to analysing and training recurrent neural networks-with an erratum note. Bonn, Germany: German National Research Center for Information Technology GMD Technical Report 148, 13 (2001).
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