Fabry-Perot Lasers as Enablers for Parallel Reservoir Computing
- URL: http://arxiv.org/abs/2005.14261v2
- Date: Thu, 28 Jan 2021 14:00:13 GMT
- Title: Fabry-Perot Lasers as Enablers for Parallel Reservoir Computing
- Authors: Adonis Bogris, Charis Mesaritakis, Stavros Deligiannidis, Pu Li
- Abstract summary: We introduce the use of Fabry-Perot (FP) lasers as potential neuromorphic computing machines with parallel processing capabilities.
We demonstrate the potential for scaling up the processing power at longitudinal mode granularity and perform real-time processing for signal equalization in 25 Gbaud intensity modulation direct detection optical communication systems.
- Score: 3.360730781782703
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We introduce the use of Fabry-Perot (FP) lasers as potential neuromorphic
computing machines with parallel processing capabilities. With the use of
optical injection between a master FP laser and a slave FP laser under feedback
we demonstrate the potential for scaling up the processing power at
longitudinal mode granularity and perform real-time processing for signal
equalization in 25 Gbaud intensity modulation direct detection optical
communication systems. We demonstrate the improvement of classification
performance as the number of nodes increases and the capability of simultaneous
processing of arbitrary data streams. Extensive numerical simulations show that
up to 8 longitudinal modes in typical Fabry-Perot lasers can be leveraged so as
to enhance classification performance.
Related papers
- Towards Neural-Network-based optical temperature sensing of Semiconductor Membrane External Cavity Laser [0.0]
A machine-learning non-contact method to determine the temperature of a laser gain medium via its laser emission is presented.
The training of the feed-forward Neural Network (NN) enables the prediction of the device's properties solely from spectral data.
arXiv Detail & Related papers (2024-10-29T20:49:29Z) - Optical training of large-scale Transformers and deep neural networks with direct feedback alignment [48.90869997343841]
We experimentally implement a versatile and scalable training algorithm, called direct feedback alignment, on a hybrid electronic-photonic platform.
An optical processing unit performs large-scale random matrix multiplications, which is the central operation of this algorithm, at speeds up to 1500 TeraOps.
We study the compute scaling of our hybrid optical approach, and demonstrate a potential advantage for ultra-deep and wide neural networks.
arXiv Detail & Related papers (2024-09-01T12:48:47Z) - Inverse design of photonic surfaces on Inconel via multi-fidelity machine learning ensemble framework and high throughput femtosecond laser processing [0.6125806862740051]
We demonstrate a multi-fidelity (MF) machine learning ensemble framework for the inverse design of photonic surfaces.
The MF ensemble combines an initial low fidelity model for generating design solutions, with a high fidelity model that refines these solutions through local optimization.
Our approach provides a powerful tool for advancing the inverse design of photonic surfaces in energy harvesting applications.
arXiv Detail & Related papers (2024-06-03T15:59:19Z) - A scalable narrow linewidth high power laser for barium ion optical
qubit [0.0]
As quantum computing endeavors scale up in qubit number, the demand for higher laser power with ultra-narrow linewidth becomes imperative.
This study explores the effectiveness of Thulium-doped fiber amplifiers as a viable solution for addressing optical qubit transitions in trapped barium ion qubits.
We demonstrate that by performing high-fidelity gates on the qubit while introducing minimal intensity noise, TDFAs do not significantly broaden the linewidth of the seed lasers.
arXiv Detail & Related papers (2023-12-06T09:56:59Z) - Hyperspectral In-Memory Computing with Optical Frequency Combs and
Programmable Optical Memories [0.0]
Machine learning has amplified the demand for extensive matrix-vector multiplication operations.
We propose a hyperspectral in-memory computing architecture that integrates space multiplexing with frequency multiplexing of optical frequency combs.
We have experimentally demonstrated multiply-accumulate operations with higher than 4-bit precision in both matrix-vector and matrix-matrix multiplications.
arXiv Detail & Related papers (2023-10-17T06:03:45Z) - Phase Randomness in a Semiconductor Laser: the Issue of Quantum Random
Number Generation [83.48996461770017]
This paper describes theoretical and experimental methods for estimating the degree of phase randomization in a gain-switched laser.
We show that the interference signal remains quantum in nature even in the presence of classical phase drift in the interferometer.
arXiv Detail & Related papers (2022-09-20T14:07:39Z) - Machine Learning-Driven Process of Alumina Ceramics Laser Machining [0.0]
An intelligent strategy is to employ machine learning (ML) techniques to capture the relationship between picosecond laser machining parameters.
Laser parameters such as beam amplitude and frequency, scanner passing speed and the number of passes over the surface, are used for predicting the depth, top width, and bottom width of the engraved channels.
Neural Networks (NN) are the most efficient in predicting the outputs.
arXiv Detail & Related papers (2022-06-13T22:35:14Z) - Invertible Surrogate Models: Joint surrogate modelling and
reconstruction of Laser-Wakefield Acceleration by invertible neural networks [55.41644538483948]
Invertible neural networks are a recent technique in machine learning.
We will be introducing invertible surrogate models that approximate complex forward simulation of the physics involved in laser plasma accelerators: iLWFA.
arXiv Detail & Related papers (2021-06-01T12:26:10Z) - Rapid characterisation of linear-optical networks via PhaseLift [51.03305009278831]
Integrated photonics offers great phase-stability and can rely on the large scale manufacturability provided by the semiconductor industry.
New devices, based on such optical circuits, hold the promise of faster and energy-efficient computations in machine learning applications.
We present a novel technique to reconstruct the transfer matrix of linear optical networks.
arXiv Detail & Related papers (2020-10-01T16:04:22Z) - Photonic reservoir computer based on frequency multiplexing [56.663315405998354]
We report a photonic implementation of a reservoir computer that exploits frequency domain multiplexing to encode neuron states.
The system processes 25 comb lines simultaneously (i.e. 25 neurons), at a rate of 20 MHz.
arXiv Detail & Related papers (2020-08-25T19:30:42Z) - Predictive modeling approaches in laser-based material processing [59.04160452043105]
This study aims to automate and forecast the effect of laser processing on material structures.
The focus is centred on the performance of representative statistical and machine learning algorithms.
Results can set the basis for a systematic methodology towards reducing material design, testing and production cost.
arXiv Detail & Related papers (2020-06-13T17:28:52Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.