Multichannel tunable dispersion compensation using all-pass multicavity etalons

Moss, David J.; Mclaughlin, Sheldon; Randall, G.; Lamont, Michael R. E.; Ardekani, Maryam; Colbourne, Paul; Kiran, Shiva; Hulse, Charles A.

doi:10.1109/ofc.2002.1036255

“…This did not, however, have any effect on the speed or throughput of our system. Moreover, in fact this can be dramatically reduced or virtually eliminatedeasily to less than 200 psjust by using any type of compact device that can replace the dispersive delay of the fibre, such as sampled Bragg gratings or etalon based tuneable dispersion compensators [99][100][101][102][103].…”

Section: Resultsmentioning

confidence: 99%

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

Tan¹,

Xu²,

Moss³

2021

Preprint

0

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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.

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“…This did not, however, have any effect on the speed or throughput of our system. Moreover, in fact this can be dramatically reduced or virtually eliminated -easily to less than 200 ps -just by using any type of compact device that can replace the dispersive delay of the bre, such as sampled Bragg gratings or etalon based tuneable dispersion compensators [99][100][101][102][103] and other approaches [104][105][106][107].…”

Section: Resultsmentioning

confidence: 99%

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

Tan¹,

Xu²,

Moss³

2021

Preprint

0

View full text Add to dashboard Cite

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.

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“…Although we had a non-trivial optical latency of 0.11 μs introduced by dispersive fibre spool, this did not affect the operational speed. Moreover, the latency of the delay function can be virtually eliminated (to < 200 ps) by using integrated highly dispersive devices such as photonic crystals or customized chirped Bragg gratings [92] or even tunable dispersion compensators [93][94][95][96]. Finally, current nanofabrication techniques can enable significantly higher levels of integration of the convolutional accelerator.…”

Section: Discussionmentioning

confidence: 99%

Photonic convolutional accelerator operating at Tera-OPs speeds for neural networks with Kerr microcombs

Moss¹

2021

Preprint

0

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<p>Convolutional neural networks (CNNs), inspired by biological visual cortex systems, are a powerful category of artificial neural networks that can extract the hierarchical features of raw data to greatly reduce the network parametric complexity and enhance the predicting accuracy. They are of significant interest for machine learning tasks such as computer vision, speech recognition, playing board games and medical diagnosis [1-7]. Optical neural networks offer the promise of dramatically accelerating computing speed to overcome the inherent bandwidth bottleneck of electronics. Here, we demonstrate a universal optical vector convolutional accelerator operating beyond 10 Tera-OPS (TOPS - operations per second), generating convolutions of images of 250,000 pixels with 8-bit resolution for 10 kernels simultaneously — enough for facial image recognition. We then use the same hardware to sequentially form a deep optical CNN with ten output neurons, achieving successful recognition of full 10 digits with 900 pixel handwritten digit images with 88% accuracy. Our results are based on simultaneously interleaving temporal, wavelength and spatial dimensions enabled by an integrated microcomb source. This approach is scalable and trainable to much more complex networks for demanding applications such as unmanned vehicle and real-time video recognition. <i> </i></p>

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Multichannel tunable dispersion compensation using all-pass multicavity etalons

Cited by 46 publications

References 2 publications

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

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

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

Photonic convolutional accelerator operating at Tera-OPs speeds for neural networks with Kerr microcombs

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