8 March 2014 Towards optoelectronic architectures for integrated neuromorphic computers
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Abstract
We investigate theoretically and experimentally the computational properties of an optoelectronic neuromorphic processor based on a complex nonlinear dynamics. This neuromorphic approach is based on a new paradigm of or reservoir computing, which is intrinsically different from the concept of Turing machines. It essentially consists in expanding the input information to be processed into a higher dimensional phase space, through the nonlinear transient response of a complex dynamics excited by the input information. The computed output is then extracted via a linear separation of the transient trajectory in the complex phase space, performed through a learning phase consisting of the resolution of a regression problem. We here investigate an architecture for photonic neuromorphic computing via these complex nonlinear dynamical transients. A versatile photonic nonlinear transient computer based on a multiple-delay is reported. Its hybrid analogue and digital architecture allows for an easy reconfiguration, and for direct implementation of in-line processing. Its computational efficiency in parameter space is also analyzed, and the computational performance of this system is successfully evaluated on a standard spoken digit recognition task. We then discuss the pathways that can lead to its effective integration.
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Romain Martinenghi, Antonio Baylon Fuentes, Maxime Jacquot, Yanne K. Chembo, Laurent Larger, "Towards optoelectronic architectures for integrated neuromorphic computers", Proc. SPIE 8989, Smart Photonic and Optoelectronic Integrated Circuits XVI, 89890K (8 March 2014); doi: 10.1117/12.2038347; https://doi.org/10.1117/12.2038347
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