Recent advances in physical reservoir computing: A review

Understanding the fundamental relationships between physics and its information-processing capability has been an active research topic for many years. Physical reservoir computing is a recently introduced framework that allows one to exploit the complex dynamics of physical systems as information-processing devices. This framework is particularly suited for edge computing devices, in which information processing is incorporated at the edge (e.g., into sensors) in a decentralized manner to reduce the adaptation delay caused by data transmission overhead. This paper aims to illustrate the potentials of the framework using examples from soft robotics and to provide a concise overview focusing on the basic motivations for introducing it, which stem from a number of fields, including machine learning, nonlinear dynamical systems, biological science, materials science, and physics.

https://iopscience.iop.org/article/10.35848/1347-4065/ab8d4f/pdf

Physical reservoir computing—an introductory perspective

In the emerging Internet of things cyber-physical system-embedded society, big data analytics needs huge computing capability with better energy efficiency. Coming to the end of Moore’s law of the electronic integrated circuit and facing the throughput limitation in parallel processing governed by Amdahl’s law, there is a strong motivation behind exploring a novel frontier of data processing in post-Moore era. Optical fiber transmissions have been making a remarkable advance over the last three decades. A record aggregated transmission capacity of the wavelength division multiplexing system per a single-mode fiber has reached 115 Tbit/s over 240 km. It is time to turn our attention to data processing by photons from the data transport by photons. A photonic accelerator (PAXEL) is a special class of processor placed at the front end of a digital computer, which is optimized to perform a specific function but does so faster with less power consumption than an electronic general-purpose processor. It can process images or time-serial data either in an analog or digital fashion on a real-time basis. Having had maturing manufacturing technology of optoelectronic devices and a diverse array of computing architectures at hand, prototyping PAXEL becomes feasible by leveraging on, e.g., cutting-edge miniature and power-efficient nanostructured silicon photonic devices. In this article, first the bottleneck and the paradigm shift of digital computing are reviewed. Next, we review an array of PAXEL architectures and applications, including artificial neural networks, reservoir computing, pass-gate logic, decision making, and compressed sensing. We assess the potential advantages and challenges for each of these PAXEL approaches to highlight the scope for future work toward practical implementation.

Novel frontier of photonics for data processing—Photonic accelerator

Photonic reservoir computing is a new paradigm for performing high-speed prediction and classification tasks in an efficient manner. The major challenge for the miniaturization of photonic reservoir computing is the need for the use of photonic integrated circuits. Herein, we experimentally demonstrate reservoir computing using a photonic integrated circuit with a semiconductor laser and a short external cavity. We propose a method to increase the number of virtual nodes in delayed feedback using short node intervals and outputs from multiple delay times. We perform time-series prediction and nonlinear channel equalization tasks using reservoir computing with the photonic integrated circuit. We show that the photonic integrated circuit with optical feedback outperforms the photonic integrated circuit without optical feedback for prediction tasks. To enhance the memory effect we feed past input signals in the current input data and demonstrate successful performance in an n-step-ahead prediction task.

Compact reservoir computing with a photonic integrated circuit.

We propose a scheme for reservoir computing using multiple semiconductor lasers with optical feedback arranged in parallel on a photonic integrated circuit, and we investigate the performance of reservoir computing numerically. The virtual nodes are obtained from the temporal waveforms of the outputs of the parallel reservoir lasers. We test the chaotic time-series prediction task, memory capacity, and nonlinear channel equalization task to investigate the performance of reservoir computing. We found that our scheme using multiple lasers outperforms that using a single laser with multiple delay times. Large memory capacity can also be obtained for the multiple lasers. Finally, we investigate the effect of parameter mismatch of the multiple lasers on reservoir computing performance.

Reservoir Computing Using Multiple Lasers With Feedback on a Photonic Integrated Circuit

We experimentally investigate delay-based photonic reservoir computing using semiconductor lasers with optical feedback and injection. We apply different types of temporal mask signals, such as digital, chaos, and colored-noise mask signals, as the weights between the input signal and the virtual nodes in the reservoir. We evaluate the performance of reservoir computing by using a time-series prediction task for the different mask signals. The chaos mask signal shows superior performance than that of the digital mask signals. However, similar prediction errors can be achieved for the chaos and colored-noise mask signals. Mask signals with larger amplitudes result in better performance for all mask signals in the range of the amplitude accessible in our experiment. The performance of reservoir computing is strongly dependent on the cut-off frequency of the colored-noise mask signals, which is related to the resonance of the relaxation oscillation frequency of the laser used as the reservoir.

Kosuke Takano

Papers

Impact of input mask signals on delay-based photonic reservoir computing with semiconductor lasers.

Compact reservoir computing with a photonic integrated circuit.