Reservoir computing and decision making using laser dynamics for photonic accelerator

Uchida, Atsushi; Kanno, Kazutaka; Sunada, Satoshi; Naruse, Makoto

doi:10.35848/1347-4065/ab7860

Cited by 8 publications

(3 citation statements)

References 37 publications

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“…The tug-of-war method provides faster decision making than software algorithms [7]. Recently, photonic implementations of the tug-of-war method have been demonstrated using quantum dots [11,12], single photons [13], entangled photons [14], and chaotic semiconductor lasers [15][16][17][18][19][20][21][22][23]. Furthermore, photonic decision making of the multi-armed bandit problem has been reported using chaotic temporal waveforms with a threshold [15][16][17], lag synchronization of chaos in coupled semiconductor lasers [18], laser network dynamics [19,20], and mode-switching dynamics in a ring-cavity laser [21].…”

Section: Introductionmentioning

confidence: 99%

Adaptive decision making using a chaotic semiconductor laser for multi-armed bandit problem with time-varying hit probabilities

Oda

Mihana

Kanno

et al. 2022

NOLTA

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We numerically demonstrate the principle of adaptive decision making for solving multi-armed bandit problems in dynamically changing reward environments. We use the tugof-war method by comparing a threshold and a chaotic temporal waveform generated from a semiconductor laser observed in an experiment. We propose a method for detecting dynamic changes in hit probabilities by evaluating short-term standard deviations of the estimated hit probabilities. Furthermore, the threshold is forced to be initialized when changes in the hit probabilities are detected. We perform adaptive decision making in time-varying hit probabilities, including cases in which the differences in the hit probabilities are small. The proposed method paves the way for ultrafast photonic decision making in dynamically changing environments for various applications, such as cognitive wireless communications and robot control using reinforcement learning.

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Section: Introductionmentioning

confidence: 99%

Adaptive decision making using a chaotic semiconductor laser for multi-armed bandit problem with time-varying hit probabilities

Oda

Mihana

Kanno

et al. 2022

NOLTA

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“…Among various photonic neural network architectures, photonic reservoir computing (RC) has recently gained increasing research interest [24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41]. Reservoir computers use networks of random connections and nonlinearities, called reservoirs.…”

Section: Introductionmentioning

confidence: 99%

Photonic neural field on a silicon chip: large-scale, high-speed neuro-inspired computing and sensing

Sunada

Uchida

2021

Preprint

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Photonic neural networks have significant potential for high-speed neural processing with low latency and ultralow energy consumption. However, the on-chip implementation of a large-scale neural network is still challenging owing to its low scalability. Herein, we propose the concept of a photonic neural field and implement it experimentally on a silicon chip to realize highly scalable neuro-inspired computing. In contrast to existing photonic neural networks, the photonic neural field is a spatially continuous field that nonlinearly responds to optical inputs, and its high spatial degrees of freedom allow for large-scale and high-density neural processing on a millimeter-scale chip. In this study, we use the on-chip photonic neural field as a reservoir of information and demonstrate a high-speed chaotic time-series prediction with low errors using a training approach similar to reservoir computing. We discuss that the photonic neural field is potentially capable of executing more than one peta multiply-accumulate operations per second for a single input wavelength on a footprint as small as a few square millimeters. In addition to processing, the photonic neural field can be used for rapidly sensing the temporal variation of an optical phase, facilitated by its high sensitivity to optical inputs. The merging of optical processing with optical sensing paves the way for an end-to-end data-driven optical sensing scheme.

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“…The use of photonic systems for RC implementation is expected to accelerate recurrent neural network processing with low energy costs [10,11]. Most photonic RC systems are based on delayed feedback structures, which can be simply realized using a nonlinear device with a time-delayed feedback loop [12,13,14,15,16,17,18]. Based on a time-multiplexing technique [19,20], the sampled signals of the nonlinear device are used as virtual neurons (network nodes) for information processing.…”

Section: Introductionmentioning

confidence: 99%

Using multidimensional speckle dynamics for high-speed, large-scale, parallel photonic computing

Sunada¹,

Kanno²,

Uchida³

2020

Opt. Express

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The recent rapid increase in demand for data processing has resulted in the need for novel machine learning concepts and hardware. Physical reservoir computing and an extreme learning machine are novel computing paradigms based on physical systems themselves, where the high dimensionality and nonlinearity play a crucial role in the information processing. Herein, we propose the use of multidimensional speckle dynamics in multimode fibers for information processing, where input information is mapped into the space, frequency, and time domains by an optical phase modulation technique. The speckle-based mapping of the input information is high-dimensional and nonlinear and can be realized at the speed of light; thus, nonlinear time-dependent information processing can successfully be achieved at fast rates when applying a reservoir-computing-like-approach. As a proof-of-concept, we experimentally demonstrate chaotic time-series prediction at input rates of 12.5 Gigasamples per second. Moreover, we show that owing to the passivity of multimode fibers, multiple tasks can be simultaneously processed within a single system, i.e., multitasking. These results offer a novel approach toward realizing parallel, high-speed, and large-scale photonic computing.

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Reservoir computing and decision making using laser dynamics for photonic accelerator

Cited by 8 publications

References 37 publications

Adaptive decision making using a chaotic semiconductor laser for multi-armed bandit problem with time-varying hit probabilities

Adaptive decision making using a chaotic semiconductor laser for multi-armed bandit problem with time-varying hit probabilities

Photonic neural field on a silicon chip: large-scale, high-speed neuro-inspired computing and sensing

Using multidimensional speckle dynamics for high-speed, large-scale, parallel photonic computing

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