Novel linear and nonlinear optical signal processing for ultra-high bandwidth communications

The thesis is articulated around the theme of ultra-wide bandwidth single channel signals. It focuses on the two main topics of transmission and processing of information by techniques compatible with high baudrates. The processing schemes introduced combine new linear and nonlinear optical platforms such as Fourier-domain programmable optical processors and chalcogenide chip waveguides, as well as the concept of neural network. Transmission of data is considered in the context of medium distance links of Optical Time Division Multiplexed (OTDM) data subject to environmental fluctuations. We experimentally demonstrate simultaneous compensation of differential group delay and multiple orders of dispersion at symbol rates of 640 Gbaud and 1.28 Tbaud. Signal processing at high bandwidth is envisaged both in the case of elementary post-transmission analog error mitigation and in the broader field of optical computing for high level operations (“optical processor”). A key innovation is the introduction of a novel four-wave mixing scheme implementing a dot-product operation between wavelength multiplexed channels. In particular, it is demonstrated for low-latency hash-key based all-optical error detection in links encoded with advanced modulation formats. Finally, the work presents groundbreaking concepts for compact implementation of an optical neural network as a programmable multi-purpose processor. The experimental architecture can implement neural networks with several nodes on a single optical nonlinear transfer function implementing functions such as analog-to-digital conversion. The particularity of the thesis is the new approaches to optical signal processing that potentially enable high level operations using simple optical hardware and limited cascading of components

Analog readout for optical reservoir computers

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.Comment: to appear in NIPS 201

Single parameter optimization for simultaneous automatic compensation of multiple orders of dispersion for a 1.28 Tbaud signal

We report the demonstration of automatic higher-order dispersion compensation for the transmission of 275 fs pulses associated with a Tbaud Optical Time Division Multiplexed (OTDM) signal. Our approach achieves simultaneous automatic compensation for 2nd, 3rd and 4th order dispersion using an LCOS spectral pulse shaper (SPS) as a tunable dispersion compensator and a dispersion monitor made of a photonic-chip-based all-optical RF-spectrum analyzer. The monitoring approach uses a single parameter measurement extracted from the RF-spectrum to drive a multidimensional optimization algorithm. Because these pulses are highly sensitive to fluctuations in the GVD and higher orders of chromatic dispersion, this work represents a key result towards practical transmission of ultrashort optical pulses. The dispersion can be adapted on-the-fly for a 1.28 Tbaud signal at any place in the transmission line using a black box approach

Reservoir Computing: a Photonic Neural Network for Information Processing

Vol. 7728 77280B-1info:eu-repo/semantics/publishe

Analog readout for optical reservoir computers

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.info:eu-repo/semantics/publishe

Towards fully analog hardware reservoir computing for speech recognition.

info:eu-repo/semantics/publishe

De l’utilisation d’un oscillateur optoélectronique comme reservoir computer

Recueil des communicationsposter A1.28info:eu-repo/semantics/publishe

Artificial Intelligence at Light Speed: towards Opto-Electronic Reservoir Computing

Available at: ftp://[email protected]/outgoing/Fabrice/BPHY/BPhy_2010_3.pdfinfo:eu-repo/semantics/publishe

Recent Advances in Optical Reservoir Computing

Reservoir Computing is a novel computing paradigm which uses a nonlinear recurrent dynamical system to carry out information processing. Here we will present an optoelectronic and an all-optical implementation of Reservoir Computers based on an architecture with a single nonlinear node and a delay loop. Moreover we will present an optical analogue readout which makes our reservoir computer a potential standalone solution after training. Our works show that, within the Reservoir Computing paradigm, all-optical computing with state-of-the-art performance is possible as a self-contained solution. © 2013 IEEE.info:eu-repo/semantics/publishe