Accelerating photonic computing by bandwidth enhancement of a time-delay reservoir

Estébanez, Irene; Schwind, Janek; Fischer, Ingo; Argyris, Apostolos

doi:10.1515/nanoph-2020-0184

Cited by 22 publications

(9 citation statements)

References 40 publications

(52 reference statements)

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“…Figure 4(b) illustrates a photonic RC implementation based on semiconductor lasers that have been used e.g. to decode coherent optical communication signals at high speeds [32].…”

Section: Delay-based Reservoir Computingmentioning

confidence: 99%

Nonlinear photonic dynamical systems for unconventional computing

Brunner

Larger

Soriano

2022

NOLTA

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Driven by the remarkable breakthroughs during the past decade, photonics neural networks have experienced a revival. Here, we provide a general overview of progress over the past decade, and sketch a roadmap of important future developments. We focus on photonic implementations of the reservoir computing machine learning paradigm, which offers a conceptually simple approach that is amenable to hardware implementations. In particular, we provide an overview of photonic reservoir computing implemented via either spatio temporal or delay dynamical systems. Going beyond reservoir computing, we discuss recent advances and future challenges of photonic implementations of deep neural networks, of the quest for learning methods that are hardware-friendly as well as realizing autonomous photonic neural networks, i.e. with minimal digital electronic auxiliary hardware.

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“…Figure 4(b) illustrates a photonic RC implementation based on semiconductor lasers that have been used e.g. to decode coherent optical communication signals at high speeds [32].…”

Section: Delay-based Reservoir Computingmentioning

confidence: 99%

Nonlinear photonic dynamical systems for unconventional computing

Brunner

Larger

Soriano

2022

NOLTA

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“…Semiconductor lasers react highly nonlinearly to optical injection, and previously we showed that this nonlinear response can be harvested to implement the nonlinear nodes of an ANN [26,27]. The feasibility of this concept was shown numerically for 33 GHz [28]. However, here each neuron is physically implemented in parallel via the LA-VCSEL's complex and high-dimensional multimode field.…”

Section: A Photonic Ann In a Complex And Continuous Nonlinear Mediamentioning

confidence: 99%

A complete, parallel, and autonomous photonic neural network in a semiconductor multimode laser

Porté

Skalli

Haghighi

et al. 2021

Emerging Topics in Artificial Intelligence (ETAI) 2021

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Neural networks are one of the disruptive computing concepts of our time. However, they fundamentally differ from classical, algorithmic computing. These differences result in equally fundamental, severe and relevant challenges for neural network computing using current computing substrates. Neural networks urge for parallelism across the entire processor and for a co-location of memory and arithmetic, i.e. beyond von Neumann architectures. Parallelism in particular made photonics a highly promising platform, yet until now scalable and integratable concepts are scarce. Here, we demonstrate for the first time how a fully parallel and fully implemented photonic neural network can be realized by spatially multiplexing neurons across the complex optical near-field of a semiconductor multimode laser. Discrete spatial sampling defines ∼90 nodes on the surface of a large-area vertical cavity surface emitting laser that is optically injected with the artificial neural networks input information. Importantly, all neural network connections are realized in hardware, and our processor produces results without pre-or post-processing. Input and output weights are realized via the complex transmission matrix of a multimode fiber and a digital micro-mirror array, respectively. We train the readout weights to perform 2-bit header recognition, a 2-bit XOR logical function and 2-bit digital to analog conversion, and obtain < 0.9 × 10 −3 and 2.9 × 10 −2 error rates for digit recognition and XOR, respectively. Finally, the digital to analog conversion can be realized with a standard deviation of only 5.4 × 10 −2 . Crucially, our proof-of-concept system is scalable to much larger sizes and to bandwidths in excess of 20 GHz.

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“…This generic chip can perform arbitrary Boolean logic operations with memory as well as 5-bit header recognition up to 12.5 Gbit s -1 , without power consumption in the reservoir and it can also perform spoken digit recognition. Delay-based reservoirs have been implemented using two different approaches: optoelectronic reservoirs that use laser sources, optical fibres [93] and Mach-Zehnder modulators, and all-optical reservoirs that are based on semiconductor lasers, semiconductor optical amplifiers or passive optical cavities [89]. An example of a delay-based reservoir is described by Dejonckheere et al [94] and it is based on a fully passive nonlinearity, namely the saturable absorption of a semiconductor mirror placed in a ring-like optical cavity.…”

Section: Photonic Reservoir Computingmentioning

confidence: 99%