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

Training Passive Photonic Reservoirs with Integrated Optical Readout

As Moore's law comes to an end, neuromorphic approaches to computing are on the rise. One of these, passive photonic reservoir computing, is a strong candidate for computing at high bitrates (> 10 Gbps) and with low energy consumption. Currently though, both benefits are limited by the necessity to perform training and readout operations in the electrical domain. Thus, efforts are currently underway in the photonic community to design an integrated optical readout, which allows to perform all operations in the optical domain. In addition to the technological challenge of designing such a readout, new algorithms have to be designed in order to train it. Foremost, suitable algorithms need to be able to deal with the fact that the actual on-chip reservoir states are not directly observable. In this work, we investigate several options for such a training algorithm and propose a solution in which the complex states of the reservoir can be observed by appropriately setting the readout weights, while iterating over a predefined input sequence. We perform numerical simulations in order to compare our method with an ideal baseline requiring full observability as well as with an established black-box optimization approach (CMA-ES).Comment: Accepted for publication in IEEE Transactions on Neural Networks and Learning Systems (TNNLS-2017-P-8539.R1), copyright 2018 IEEE. This research was funded by the EU Horizon 2020 PHRESCO Grant (Grant No. 688579) and the BELSPO IAP P7-35 program Photonics@be. 11 pages, 9 figure

Memristor models for machine learning

In the quest for alternatives to traditional CMOS, it is being suggested that digital computing efficiency and power can be improved by matching the precision to the application. Many applications do not need the high precision that is being used today. In particular, large gains in area- and power efficiency could be achieved by dedicated analog realizations of approximate computing engines. In this work, we explore the use of memristor networks for analog approximate computation, based on a machine learning framework called reservoir computing. Most experimental investigations on the dynamics of memristors focus on their nonvolatile behavior. Hence, the volatility that is present in the developed technologies is usually unwanted and it is not included in simulation models. In contrast, in reservoir computing, volatility is not only desirable but necessary. Therefore, in this work, we propose two different ways to incorporate it into memristor simulation models. The first is an extension of Strukov's model and the second is an equivalent Wiener model approximation. We analyze and compare the dynamical properties of these models and discuss their implications for the memory and the nonlinear processing capacity of memristor networks. Our results indicate that device variability, increasingly causing problems in traditional computer design, is an asset in the context of reservoir computing. We conclude that, although both models could lead to useful memristor based reservoir computing systems, their computational performance will differ. Therefore, experimental modeling research is required for the development of accurate volatile memristor models.Comment: 4 figures, no tables. Submitted to neural computatio

Brain-inspired nanophotonic spike computing:challenges and prospects

Nanophotonic spiking neural networks (SNNs) based on neuron-like excitable subwavelength (submicrometre) devices are of key importance for realizing brain-inspired, power-efficient artificial intelligence (AI) systems with high degree of parallelism and energy efficiency. Despite significant advances in neuromorphic photonics, compact and efficient nanophotonic elements for spiking signal emission and detection, as required for spike-based computation, remain largely unexplored. In this invited perspective, we outline the main challenges, early achievements, and opportunities toward a key-enabling photonic neuro-architecture using III-V/Si integrated spiking nodes based on nanoscale resonant tunnelling diodes (nanoRTDs) with folded negative differential resistance. We utilize nanoRTDs as nonlinear artificial neurons capable of spiking at high-speeds. We discuss the prospects for monolithic integration of nanoRTDs with nanoscale light-emitting diodes and nanolaser diodes, and nanophotodetectors to realize neuron emitter and receiver spiking nodes, respectively. Such layout would have a small footprint, fast operation, and low power consumption, all key requirements for efficient nano-optoelectronic spiking operation. We discuss how silicon photonics interconnects, integrated photorefractive interconnects, and 3D waveguide polymeric interconnections can be used for interconnecting the emitter-receiver spiking photonic neural nodes. Finally, using numerical simulations of artificial neuron models, we present spike-based spatio-temporal learning methods for applications in relevant AI-based functional tasks, such as image pattern recognition, edge detection, and SNNs for inference and learning. Future developments in neuromorphic spiking photonic nanocircuits, as outlined here, will significantly boost the processing and transmission capabilities of next-generation nanophotonic spike-based neuromorphic architectures for energy-efficient AI applications. This perspective paper is a result of the European Union funded research project ChipAI in the frame of the Horizon 2020 Future and Emerging Technologies Open programme.</p

Direct coupling of nonlinear integrated cavities for all-optical reservoir computing

We consider theoretically a network of directly coupled optical microcavities to implement a space-multiplexed optical neural network in an integrated nanophotonic circuit. Nonlinear photonic network integrations based on direct coupling ensures a highly dense integration, reducing the chip footprint by several orders of magnitude compared to other implementations. Different nonlinear effects inherent to such microcavities are studied when used for realizing an all-optical autonomous computing substrate, here based on the reservoir computing concept. We provide an in-depth analysis of the impact of basic microcavity parameters on computational metrics of the system, namely, the dimensionality and the consistency. Importantly, we find that differences between frequencies and bandwidths of supermodes formed by the direct coupling is the determining factor of the reservoir's dimensionality and its scalability. The network's dimensionality can be improved with frequency-shifting nonlinear effects such as the Kerr effect, while two-photon absorption has an opposite effect. Finally, we demonstrate in simulation that the proposed reservoir is capable of solving the Mackey-Glass prediction and the optical signal recovery tasks at GHz timescale

A neuromorphic silicon photonics nonlinear equalizer for optical communications with intensity modulation and direct detection

We present the design and numerical study of a nonlinear equalizer for optical communications based on silicon photonics and reservoir computing. The proposed equalizer leverages the optical information processing capabilities of integrated photonic reservoirs to combat distortions both in metro links of a few hundred kilometers and in high-speed short-reach intensity-modulation-direct-detection links. We show nonlinear compensation in unrepeated metro links of up to 200 km that outperform electrical feedforward equalizers based equalizers, and ultimately any linear compensation device. For a high-speed short-reach 40Gb/s link based on a distributed feedback laser and an electroabsorptive modulator, and considering a hard decision forward error correction limit of 0.2 x 10(-2), we can increase the reach by almost 10 km. Our equalizer is compact (only 16 nodes) and operates in the optical domain without the need for complex electronic DSP, meaning its performance is not bandwidth constrained. The approach is, therefore, a viable candidate even for equalization techniques far beyond 100G optical communication links