scholarly journals Time Wavelength Interleaving Perceptron at 12 Giga-Ops/s with a Kerr Soliton Crystal Microcomb for Optical Neural Networks

2021 ◽  
Author(s):  
Mengxi Tan ◽  
Xingyuan Xu ◽  
David Moss
Keyword(s):  
Neural Networks ◽  
Matrix Multiplication ◽  
Cell Detection ◽  
Digit Recognition ◽  
High Speeds ◽  
Novel Approach ◽  
Handwritten Digit ◽  
Deep Learning Network ◽  
Cancer Cell Detection ◽  
Massive Data Processing

Optical artificial neural networks (ONNs) have significant potential for ultra-high computing speed and energy efficiency. We report a novel approach to ONNs that uses integrated Kerr optical micro-combs. This approach is programmable and scalable and is capable of reaching ultra-high speeds. We demonstrate the basic building block ONNs — a single neuron perceptron — by mapping synapses onto 49 wavelengths to achieve an operating speed of 11.9 x 109 operations per second, or Giga-OPS, at 8 bits per operation, which equates to 95.2 gigabits/s (Gbps). We test the perceptron on handwritten-digit recognition and cancer-cell detection — achieving over 90% and 85% accuracy, respectively. By scaling the perceptron to a deep learning network using off-the-shelf telecom technology we can achieve high throughput operation for matrix multiplication for real-time massive data processing.

scholarly journals Photonic perceptron at Giga-OP/s speeds with Kerr microcombs for scalable optical neural networks

2021 ◽  
Author(s):  
mengxi tan ◽  
xingyuan xu ◽  
David Moss
Keyword(s):  
Neural Networks ◽  
Matrix Multiplication ◽  
Cell Detection ◽  
Digit Recognition ◽  
High Speeds ◽  
Novel Approach ◽  
Handwritten Digit ◽  
Deep Learning Network ◽  
Cancer Cell Detection ◽  
Massive Data Processing

Abstract Optical artificial neural networks (ONNs) have significant potential for ultra-high computing speed and energy efficiency. We report a novel approach to ONNs that uses integrated Kerr optical micro-combs. This approach is programmable and scalable and is capable of reaching ultra-high speeds. We demonstrate the basic building block ONNs — a single neuron perceptron — by mapping synapses onto 49 wavelengths to achieve an operating speed of 11.9 x 109 operations per second, or Giga-OPS, at 8 bits per operation, which equates to 95.2 gigabits/s (Gbps). We test the perceptron on handwritten-digit recognition and cancer-cell detection — achieving over 90% and 85% accuracy, respectively. By scaling the perceptron to a deep learning network using off-the-shelf telecom technology we can achieve high throughput operation for matrix multiplication for real-time massive data processing.

scholarly journals Single perceptron operating at 12 GigaOPs based on a Kerr soliton crystal microcomb for versatile, high-speed, scalable, optical neural networks

2021 ◽  
Author(s):  
David Moss
Keyword(s):  
Neural Networks ◽  
High Speed ◽  
Matrix Multiplication ◽  
Cell Detection ◽  
Digit Recognition ◽  
High Speeds ◽  
Novel Approach ◽  
Handwritten Digit ◽  
Deep Learning Network ◽  
Cancer Cell Detection

<p>Optical artificial neural networks (ONNs) have significant potential for ultra-high computing speed and energy efficiency. We report a novel approach to ONNs that uses integrated Kerr optical micro-combs. This approach is programmable and scalable and is capable of reaching ultra-high speeds. We demonstrate the basic building block ONNs — a single neuron perceptron — by mapping synapses onto 49 wavelengths to achieve an operating speed of 11.9 x 10<sup>9</sup> operations per second, or Giga-OPS, at 8 bits per operation, which equates to 95.2 gigabits/s (Gbps). We test the perceptron on handwritten-digit recognition and cancer-cell detection — achieving over 90% and 85% accuracy, respectively. By scaling the perceptron to a deep learning network using off-the-shelf telecom technology we can achieve high throughput operation for matrix multiplication for real-time massive data processing. </p>

scholarly journals Single perceptron operating at 12 GigaOPs based on a Kerr soliton crystal microcomb for versatile, high-speed, scalable, optical neural networks

2021 ◽  
Author(s):  
David Moss
Keyword(s):  
Neural Networks ◽  
High Speed ◽  
Matrix Multiplication ◽  
Cell Detection ◽  
Digit Recognition ◽  
High Speeds ◽  
Novel Approach ◽  
Handwritten Digit ◽  
Deep Learning Network ◽  
Cancer Cell Detection

<p>Optical artificial neural networks (ONNs) have significant potential for ultra-high computing speed and energy efficiency. We report a novel approach to ONNs that uses integrated Kerr optical micro-combs. This approach is programmable and scalable and is capable of reaching ultra-high speeds. We demonstrate the basic building block ONNs — a single neuron perceptron — by mapping synapses onto 49 wavelengths to achieve an operating speed of 11.9 x 10<sup>9</sup> operations per second, or Giga-OPS, at 8 bits per operation, which equates to 95.2 gigabits/s (Gbps). We test the perceptron on handwritten-digit recognition and cancer-cell detection — achieving over 90% and 85% accuracy, respectively. By scaling the perceptron to a deep learning network using off-the-shelf telecom technology we can achieve high throughput operation for matrix multiplication for real-time massive data processing. </p>

scholarly journals Photonic perceptron operating at 12 GigaOPs based on a Kerr microcomb for high-speed, scalable, optical neural networks

2021 ◽  
Author(s):  
David Moss
Keyword(s):  
Neural Networks ◽  
High Speed ◽  
Matrix Multiplication ◽  
Cell Detection ◽  
Digit Recognition ◽  
High Speeds ◽  
Novel Approach ◽  
Handwritten Digit ◽  
Deep Learning Network ◽  
Cancer Cell Detection

Optical artificial neural networks (ONNs) have significant potential for ultra-high computing speed and energy efficiency. We report a novel approach to ONNs that uses integrated Kerr optical micro-combs. This approach is programmable and scalable and is capable of reaching ultra-high speeds. We demonstrate the basic building block ONNs — a single neuron perceptron — by mapping synapses onto 49 wavelengths to achieve an operating speed of 11.9 x 109 operations per second, or Giga-OPS, at 8 bits per operation, which equates to 95.2 gigabits/s (Gbps). We test the perceptron on handwritten-digit recognition and cancer-cell detection — achieving over 90% and 85% accuracy, respectively. By scaling the perceptron to a deep learning network using off-the-shelf telecom technology we can achieve high throughput operation for matrix multiplication for real-time massive data processing.

scholarly journals Kerr microcombs based on soliton crystals for high-speed, scalable optical neural networks

2020 ◽  
Author(s):  
David Moss
Keyword(s):  
Neural Networks ◽  
High Speed ◽  
Matrix Multiplication ◽  
Cell Detection ◽  
New Approach ◽  
Digit Recognition ◽  
High Speeds ◽  
Handwritten Digit ◽  
Deep Learning Network ◽  
Cancer Cell Detection

Optical artificial neural networks (ONNs) have significant potential for ultra-high computing speed and energy efficiency. We report a new approach to ONNs based on integrated Kerr micro-combs that is programmable, highly scalable and capable of reaching ultra-high speeds, demonstrating the building block of the ONN — a single neuron perceptron — by mapping synapses onto 49 wavelengths to achieve a single-unit throughput of 11.9 Giga-OPS at 8 bits per OP, or 95.2 Gbps. We test the perceptron on handwritten-digit recognition and cancer-cell detection — achieving over 90% and 85% accuracy, respectively. By scaling the perceptron to a deep learning network using off-the-shelf telecom technology we can achieve high throughput operation for matrix multiplication for real-time massive data processing.

scholarly journals Kerr microcombs based on soliton crystals for high-speed, scalable optical neural networks

2020 ◽  
Author(s):  
David Moss
Keyword(s):  
Neural Networks ◽  
High Speed ◽  
Matrix Multiplication ◽  
Cell Detection ◽  
New Approach ◽  
Digit Recognition ◽  
High Speeds ◽  
Handwritten Digit ◽  
Deep Learning Network ◽  
Cancer Cell Detection

Optical artificial neural networks (ONNs) have significant potential for ultra-high computing speed and energy efficiency. We report a new approach to ONNs based on integrated Kerr micro-combs that is programmable, highly scalable and capable of reaching ultra-high speeds, demonstrating the building block of the ONN — a single neuron perceptron — by mapping synapses onto 49 wavelengths to achieve a single-unit throughput of 11.9 Giga-OPS at 8 bits per OP, or 95.2 Gbps. We test the perceptron on handwritten-digit recognition and cancer-cell detection — achieving over 90% and 85% accuracy, respectively. By scaling the perceptron to a deep learning network using off-the-shelf telecom technology we can achieve high throughput operation for matrix multiplication for real-time massive data processing.

scholarly journals IEEE Microwave Photonics Conf 2020 P.26 perceptron OSF

2021 ◽  
Author(s):  
David Moss
Keyword(s):  
Matrix Multiplication ◽  
Cell Detection ◽  
New Approach ◽  
Digit Recognition ◽  
High Speeds ◽  
Handwritten Digit ◽  
Deep Learning Network ◽  
Cancer Cell Detection ◽  
Massive Data Processing ◽  
Ieee Microwave

Optical artificial neural networks (ONNs) have significant potential for ultra-high computing speed and energy efficiency. We report a new approach to ONNs based on integrated Kerr micro-combs that is programmable, highly scalable and capable of reaching ultra-high speeds, demonstrating the building block of the ONN — a single neuron perceptron — by mapping synapses onto 49 wavelengths to achieve a single-unit throughput of 11.9 Giga-OPS at 8 bits per OP, or 95.2 Gbps. We test the perceptron on handwritten-digit recognition and cancer-cell detection — achieving over 90% and 85% accuracy, respectively. By scaling the perceptron to a deep learning network using off-the-shelf telecom technology we can achieve high throughput operation for matrix multiplication for real-time massive data processing.

scholarly journals Soliton Crystal Microcombs for Versatile, High-Speed, Scalable Optical Neural Networks

2020 ◽  
Author(s):  
Xingyuan Xu ◽  
Mengxi Tan ◽  
David Moss
Keyword(s):  
Neural Networks ◽  
High Speed ◽  
Matrix Multiplication ◽  
Cell Detection ◽  
New Approach ◽  
Digit Recognition ◽  
High Speeds ◽  
Handwritten Digit ◽  
Deep Learning Network ◽  
Cancer Cell Detection

Optical artificial neural networks (ONNs) have significant potential for ultra-high computing speed and energy efficiency. We report a new approach to ONNs based on integrated Kerr micro-combs that is programmable, highly scalable and capable of reaching ultra-high speeds, demonstrating the building block of the ONN &mdash; a single neuron perceptron &mdash; by mapping synapses onto 49 wavelengths to achieve a single-unit throughput of 11.9 Giga-OPS at 8 bits per OP, or 95.2 Gbps. We test the perceptron on handwritten-digit recognition and cancer-cell detection &mdash; achieving over 90% and 85% accuracy, respectively. By scaling the perceptron to a deep learning network using off-the-shelf telecom technology we can achieve high throughput operation for matrix multiplication for real-time massive data processing.

scholarly journals Laser and Photonics Reviews photonic single perceptron based on time-wavelength multiplexing with a microcomb for optical neural networks

2020 ◽  
Author(s):  
David Moss
Keyword(s):  
Neural Networks ◽  
Matrix Multiplication ◽  
Direct Result ◽  
Cell Detection ◽  
Analog Computing ◽  
Aircraft Tracking ◽  
Benchmark Datasets ◽  
Handwritten Digit ◽  
Deep Learning Network ◽  
Cancer Cell Detection

Optical artificial neural networks (ONNs) — analog computing hardware tailored for machine learning [1, 2] — have significant potential for ultra-high computing speed and energy efficiency [3]. We propose a new approach to architectures for ONNs based on integrated Kerr micro-comb sources [4] that is programmable, highly scalable and capable of reaching ultra-high speeds. We experimentally demonstrate the building block of the ONN — a single neuron perceptron — by mapping synapses onto 49 wavelengths of a micro-comb to achieve a high single-unit throughput of 11.9 Giga-FLOPS at 8 bits per FLOP, corresponding to 95.2 Gbps. We test the perceptron on simple standard benchmark datasets — handwritten-digit recognition and cancer-cell detection — achieving over 90% and 85% accuracy, respectively. This performance is a direct result of the record small wavelength spacing (49GHz) for a coherent integrated microcomb source, which results in an unprecedented number of wavelengths for neuromorphic optics. Finally, we propose an approach to scaling the perceptron to a deep learning network using the same single micro-comb device and standard off-the-shelf telecommunications technology, for high-throughput operation involving full matrix multiplication for applications such as real-time massive data processing for unmanned vehicle and aircraft tracking.

scholarly journals A single photonic perceptron based on a soliton crystal microcomb for scalable, high speed, optical neural networks

2020 ◽  
Author(s):  
David Moss ◽  
xingyuan xu ◽  
mengxi tan ◽  
Jiayang Wu ◽  
Roberto Morandotti
Keyword(s):  
Neural Networks ◽  
High Speed ◽  
Matrix Multiplication ◽  
Direct Result ◽  
Cell Detection ◽  
Analog Computing ◽  
Aircraft Tracking ◽  
Benchmark Datasets ◽  
Deep Learning Network ◽  
Cancer Cell Detection

<p><b>Optical artificial neural networks (ONNs) — analog computing hardware tailored for machine learning — have significant potential for ultra-high computing speed and energy efficiency. We propose a new approach to architectures for ONNs based on integrated Kerr micro-comb sources that is programmable, highly scalable and capable of reaching ultra-high speeds. We experimentally demonstrate the building block of the ONN — a single neuron perceptron — by mapping synapses onto 49 wavelengths of a micro-comb to achieve a high single-unit throughput of 11.9 Giga-FLOPS at 8 bits per FLOP, corresponding to 95.2 Gbps. We test the perceptron on simple standard benchmark datasets — handwritten-digit recognition and cancer-cell detection — achieving over 90% and 85% accuracy, respectively. This performance is a direct result of the record small wavelength spacing (49GHz) for a coherent integrated microcomb source, which results in an unprecedented number of wavelengths for neuromorphic optics. Finally, we propose an approach to scaling the perceptron to a deep learning network using the same single micro-comb device and standard off-the-shelf telecommunications technology, for high-throughput operation involving full matrix multiplication for applications such as real-time massive data processing for unmanned vehicle and aircraft tracking. </b></p>

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