par Smerieri, Anteo 
;Duport, Francois ;Paquot, Yvan ;Schrauwen, Benjamin;Haelterman, Marc ;Massar, Serge
Référence Neural Information Processing Systems ( NIPS )(25: 3-6 décembre 2012: Lake Tahoe, Nevada, USA), Advances in Neural Information Processing Systems 25, 26th Annual Conference on Neural Information Processing Systems 2012, Curran Associates, Inc., page (953)
Publication Publié, 2012
;Duport, Francois ;Paquot, Yvan ;Schrauwen, Benjamin;Haelterman, Marc ;Massar, Serge Référence Neural Information Processing Systems ( NIPS )(25: 3-6 décembre 2012: Lake Tahoe, Nevada, USA), Advances in Neural Information Processing Systems 25, 26th Annual Conference on Neural Information Processing Systems 2012, Curran Associates, Inc., page (953)
Publication Publié, 2012
Publication dans des actes
| Résumé : | Reservoir computing is a new, powerful and flexible machine learning technique that is easily implemented in hardware. Recently, by using a time-multiplexed architecture, hardware reservoir computers have reached performance comparable to digital implementations. Operating speeds allowing for real time information operation have been reached using optoelectronic systems. At present the main performance bottleneck is the readout layer which uses slow, digital postprocessing. We have designed an analog readout suitable for time-multiplexed optoelectronic reservoir computers, capable of working in real time. The readout has been built and tested experimentally on a standard benchmark task. Its performance is better than non-reservoir methods, with ample room for further improvement. The present work thereby overcomes one of the major limitations for the future development of hardware reservoir computers. |
