Improving time series recognition and prediction with networks and ensembles of passive photonic reservoirs

As the performance increase of traditional Von-Neumann computing attenuates, new approaches to computing need to be found. A promising approach for low-power computing at high bitrates is integrated photonic reservoir computing. In the past though, the feasible reservoir size and computational power of integrated photonic reservoirs have been limited by hardware constraints. An alternative solution to building larger reservoirs is the combination of several small reservoirs to match or exceed the performance of a single bigger one. This paper summarizes our efforts to increase the available computational power by combining multiple reservoirs into a single computing architecture. We investigate several possible combination techniques and evaluate their performance using the classic XOR and header recognition tasks as well as the well-known Santa Fe chaotic laser prediction task. Our findings suggest that a new paradigm of feeding a reservoir's output into the readout structure of the next one shows consistently good results for various tasks as well as for both electrical and optical readouts and coupling schemes

Integrated Photonic Reservoir Computing with All-Optical Readout

Integrated photonic reservoir computing has been demonstrated to be able to tackle different problems because of its neural network nature. A key advantage of photonic reservoir computing over other neuromorphic paradigms is its straightforward readout system, which facilitates both rapid training and robust, fabrication variation-insensitive photonic integrated hardware implementation for real-time processing. We present our recent development of a fully-optical, coherent photonic reservoir chip integrated with an optical readout system, capitalizing on these benefits. Alongside the integrated system, we also demonstrate a weight update strategy that is suitable for the integrated optical readout hardware. Using this online training scheme, we successfully solved 3-bit header recognition and delayed XOR tasks at 20 Gbps in real-time, all within the optical domain without excess delays

Photonic neuromorphic information processing and reservoir computing

Photonic neuromorphic computing is attracting tremendous research interest now, catalyzed in no small part by the rise of deep learning in many applications. In this paper, we will review some of the exciting work that has been going in this area and then focus on one particular technology, namely, photonic reservoir computing

Experimental realization of integrated photonic reservoir computing for nonlinear fiber distortion compensation

Nonlinearity mitigation in optical fiber networks is typically handled by electronic Digital Signal Processing (DSP) chips. Such DSP chips are costly, power-hungry and can introduce high latencies. Therefore, optical techniques are investigated which are more efficient in both power consumption and processing cost. One such a machine learning technique is optical reservoir computing, in which a photonic chip can be trained on certain tasks, with the potential advantages of higher speed, reduced power consumption and lower latency compared to its electronic counterparts. In this paper, experimental results are presented where nonlinear distortions in a 32 GBPS OOK signal are mitigated to below the 0.2 x 10(-3) FEC limit using a photonic reservoir. Furthermore, the results of the reservoir chip are compared to a tapped delay line filter to clearly show that the system performs nonlinear equalisation. (C) 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreemen

All-optical readout for integrated photonic reservoir computing

Photonic neuromorphic computing has gained a lot of attention for its strong potential to deliver machine learning computation capability at high bitrates (> 32 Gbps) with very low energy consumption. Reservoir computing is one of the strong candidates that delivers a huge advantage on a real hardware implementation. However, one of the challenges is that current readout systems are the bottleneck of the high-speed link involving heavy power consumption from opto-electrical conversions. In this paper, we present our design of an integrated all-optical readout system that overcomes the challenges with optical weighting elements, which works at 32 Gbps and can deliver computation capability on-chip with one final readout signal channel. We especially compare the memory capability difference between the optical readout scheme with conventional electrical readout and show that optical readout is superior. Furthermore, this paper discusses the problem of some non-volatile optical weighting elements having limited weighting resolution. As a result, our readout system can still perform very well at an low resolution (4 bit) compared to using full resolution weighting elements

Experimental demonstration of nonlinear fibre distortion compensation with integrated photonic reservoir computing

Optical reservoir computing is a machine learning technique in which a photonic chip can be trained on classification tasks of time signals. This paper presents experimental results where linear and nonlinear fibre distortions are mitigated to below the 0.2×10 −3 FEC limit using a photonic reservoir

Photonic reservoir computing for nonlinear equalization of 64-QAM signals with a Kramers-Kronig receiver

Photonic reservoirs are machine learning based systems that boast energy efficiency and speediness. Thus they can be deployed as optical processors in fiber communication systems to aid or replace digital signal equalization. In this paper, we simulate the use of a passive photonic reservoir to target nonlinearity-induced errors originating from self-phase modulation in the fiber and from the nonlinear response of the modulator. A 64-level quadrature-amplitude modulated signal is directly detected using the recently proposed Kramers-Kronig (KK) receiver. We train the readout weights by backpropagating through the receiver pipeline, thereby providing extra nonlinearity. Statistically computed bit error rates for fiber lengths of up to 100 km fall below 1 x 10(-3) bit error rate, outperforming an optical feed-forward equalizer as a linear benchmark. This can find applications in inter-datacenter communications that benefit from the hardware simplicity of a KK receiver and the low power and low latency processing of a photonic reservoir

A power-efficient architecture for on-chip reservoir computing

Reservoir computing is a neuromorphic computing paradigm which is well suited for hardware implementations. In this work, an enhanced reservoir architecture is introduced as to lower the losses and improve mixing behaviour in silicon photonic reservoir computing designs