Bendix Schneider

All-optical reservoir computing.

Reservoir Computing is a novel computing paradigm that uses a nonlinear recurrent dynamical system to carry out information processing. Recent electronic and optoelectronic Reservoir Computers based on an architecture with a single nonlinear node and a delay loop have shown performance on standardized tasks comparable to state-of-the-art digital implementations. Here we report an all-optical implementation of a Reservoir Computer, made of off-the-shelf components for optical telecommunications. It uses the saturation of a semiconductor optical amplifier as nonlinearity. The present work shows that, within the Reservoir Computing paradigm, all-optical computing with state-of-the-art performance is possible.

Low-Loss Photonic Reservoir Computing with Multimode Photonic Integrated Circuits

We present a numerical study of a passive integrated photonics reservoir computing platform based on multimodal Y-junctions. We propose a novel design of this junction where the level of adiabaticity is carefully tailored to capture the radiation loss in higher-order modes, while at the same time providing additional mode mixing that increases the richness of the reservoir dynamics. With this design, we report an overall average combination efficiency of 61% compared to the standard 50% for the single-mode case. We demonstrate that with this design, much more power is able to reach the distant nodes of the reservoir, leading to increased scaling prospects. We use the example of a header recognition task to confirm that such a reservoir can be used for bit-level processing tasks. The design itself is CMOS-compatible and can be fabricated through the known standard fabrication procedures.

Using neural networks for high-speed blood cell classification in a holographic-microscopy flow-cytometry system

High-throughput cell sorting with flow cytometers is an important tool in modern clinical cell studies. Most cytometers use biomarkers that selectively bind to the cell, but induce significant changes in morphology and inner cell processes leading sometimes to its death. This makes label-based cell sorting schemes unsuitable for further investigation. We propose a label-free technique that uses a digital inline holographic microscopy for cell imaging and an integrated, optical neural network for high-speed classification. The perspective of dense integration makes it attractive to ultrafast, large-scale cell sorting. Network simulations for a ternary classification task (monocytes/granulocytes/lymphocytes) resulted in 89% accuracy.

Using Digital Masks to Enhance the Bandwidth Tolerance and Improve the Performance of On-Chip Reservoir Computing Systems

Reservoir computing (RC) is a computing scheme related to recurrent neural network theory. As a model for neural activity in the brain, it attracts a lot of attention, especially because of its very simple training method. However, building a functional, on-chip, photonic implementation of RC remains a challenge. Scaling delay lines down from optical fiber scale to chip scale results in RC systems that compute faster, but at the same time requires that the input signals be scaled up in speed, which might be impractical or expensive. In this brief, we show that this problem can be alleviated by a masked RC system in which the amplitude of the input signal is modulated by a binary-valued mask. For a speech recognition task, we demonstrate that the necessary input sample rate can be a factor of 40 smaller than in a conventional RC system. In addition, we also show that linear discriminant analysis and input matrix optimization is a well-performing alternative to linear regression for reservoir training.

An integrated Photonics Reservoir Computing approach to signal equalization for telecommunications

Photonic Reservoir Computing is a brain-inspired computing approach that brings the fast speeds and enormous bandwidth associated with lightwave technology together with the versatility of machine learning to enable the efficient computation of tasks requiring a finite amount of memory such as speech recognition, series prediction, header recognition etc. Broadly, our efforts focus on applying photonic reservoir computing implemented with the Silicon on Insulator (SOI) CMOS- compatible primitives to develop applications in the optical telecommunications space to take advantage of the aforementioned advantages. Specifically, this work presents our results on the implementation of a passive photonic reservoir chip that can be positioned at the receiver of a short or long metro link to invert impairments introduced to the optical transmitted signal due to a variety of imperfections and noise sources.

Neural network for blood cell classification in a holographic microscopy system

Modern clinical laboratories are equipped with high-throughput flow cytometers for fast and accurate cell sorting. Most cytometers use selective biomarkers which often induce significant changes in the cell morphology, sometimes leading to cell death. However, for purposes like cell imaging there exist label-free techniques, for example digital inline holographic microscopy. Yet the image reconstruction algorithms needed to analyze the images do not scale up easily to large numbers of cells. We suggest an integrated, optical neural network to deal with the high-speed image classification with the promise of dense integration for ultrafast, cell sorting. A ternary classification task, distinguishing between monocytes, granulocytes, and lymphocytes resulted in 89% accuracy.

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.