PhotoFourier: A Photonic Joint Transform Correlator-Based Neural Network Accelerator Article Swipe
Shurui Li
,
Hangbo Yang
,
Chee Wei Wong
,
Volker J. Sorger
,
Puneet Gupta
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2211.05276
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2211.05276
The last few years have seen a lot of work to address the challenge of low-latency and high-throughput convolutional neural network inference. Integrated photonics has the potential to dramatically accelerate neural networks because of its low-latency nature. Combined with the concept of Joint Transform Correlator (JTC), the computationally expensive convolution functions can be computed instantaneously (time of flight of light) with almost no cost. This 'free' convolution computation provides the theoretical basis of the proposed PhotoFourier JTC-based CNN accelerator. PhotoFourier addresses a myriad of challenges posed by on-chip photonic computing in the Fourier domain including 1D lenses and high-cost optoelectronic conversions. The proposed PhotoFourier accelerator achieves more than 28X better energy-delay product compared to state-of-art photonic neural network accelerators.
Related Topics
Concepts
Convolution (computer science)
Photonics
Computer science
Latency (audio)
Computation
Inference
Convolutional neural network
Artificial neural network
Throughput
Electronic engineering
Fourier transform
Joint (building)
Computational science
Artificial intelligence
Computer engineering
Algorithm
Optics
Telecommunications
Physics
Engineering
Architectural engineering
Wireless
Quantum mechanics
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2211.05276
- https://arxiv.org/pdf/2211.05276
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4308828228
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4308828228Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2211.05276Digital Object Identifier
- Title
-
PhotoFourier: A Photonic Joint Transform Correlator-Based Neural Network AcceleratorWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-11-10Full publication date if available
- Authors
-
Shurui Li, Hangbo Yang, Chee Wei Wong, Volker J. Sorger, Puneet GuptaList of authors in order
- Landing page
-
https://arxiv.org/abs/2211.05276Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2211.05276Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2211.05276Direct OA link when available
- Concepts
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Convolution (computer science), Photonics, Computer science, Latency (audio), Computation, Inference, Convolutional neural network, Artificial neural network, Throughput, Electronic engineering, Fourier transform, Joint (building), Computational science, Artificial intelligence, Computer engineering, Algorithm, Optics, Telecommunications, Physics, Engineering, Architectural engineering, Wireless, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.than | 107 |
| abstract_inverted_index.with | 38, 60 |
| abstract_inverted_index.work | 9 |
| abstract_inverted_index.(time | 55 |
| abstract_inverted_index.Joint | 42 |
| abstract_inverted_index.basis | 71 |
| abstract_inverted_index.cost. | 63 |
| abstract_inverted_index.posed | 85 |
| abstract_inverted_index.years | 3 |
| abstract_inverted_index.'free' | 65 |
| abstract_inverted_index.(JTC), | 45 |
| abstract_inverted_index.almost | 61 |
| abstract_inverted_index.better | 109 |
| abstract_inverted_index.domain | 93 |
| abstract_inverted_index.flight | 57 |
| abstract_inverted_index.lenses | 96 |
| abstract_inverted_index.light) | 59 |
| abstract_inverted_index.myriad | 82 |
| abstract_inverted_index.neural | 19, 30, 116 |
| abstract_inverted_index.Fourier | 92 |
| abstract_inverted_index.address | 11 |
| abstract_inverted_index.because | 32 |
| abstract_inverted_index.concept | 40 |
| abstract_inverted_index.nature. | 36 |
| abstract_inverted_index.network | 20, 117 |
| abstract_inverted_index.on-chip | 87 |
| abstract_inverted_index.product | 111 |
| abstract_inverted_index.Combined | 37 |
| abstract_inverted_index.achieves | 105 |
| abstract_inverted_index.compared | 112 |
| abstract_inverted_index.computed | 53 |
| abstract_inverted_index.networks | 31 |
| abstract_inverted_index.photonic | 88, 115 |
| abstract_inverted_index.proposed | 74, 102 |
| abstract_inverted_index.provides | 68 |
| abstract_inverted_index.JTC-based | 76 |
| abstract_inverted_index.Transform | 43 |
| abstract_inverted_index.addresses | 80 |
| abstract_inverted_index.challenge | 13 |
| abstract_inverted_index.computing | 89 |
| abstract_inverted_index.expensive | 48 |
| abstract_inverted_index.functions | 50 |
| abstract_inverted_index.high-cost | 98 |
| abstract_inverted_index.including | 94 |
| abstract_inverted_index.photonics | 23 |
| abstract_inverted_index.potential | 26 |
| abstract_inverted_index.Correlator | 44 |
| abstract_inverted_index.Integrated | 22 |
| abstract_inverted_index.accelerate | 29 |
| abstract_inverted_index.challenges | 84 |
| abstract_inverted_index.inference. | 21 |
| abstract_inverted_index.accelerator | 104 |
| abstract_inverted_index.computation | 67 |
| abstract_inverted_index.convolution | 49, 66 |
| abstract_inverted_index.low-latency | 15, 35 |
| abstract_inverted_index.theoretical | 70 |
| abstract_inverted_index.PhotoFourier | 75, 79, 103 |
| abstract_inverted_index.accelerator. | 78 |
| abstract_inverted_index.conversions. | 100 |
| abstract_inverted_index.dramatically | 28 |
| abstract_inverted_index.energy-delay | 110 |
| abstract_inverted_index.state-of-art | 114 |
| abstract_inverted_index.accelerators. | 118 |
| abstract_inverted_index.convolutional | 18 |
| abstract_inverted_index.optoelectronic | 99 |
| abstract_inverted_index.computationally | 47 |
| abstract_inverted_index.high-throughput | 17 |
| abstract_inverted_index.instantaneously | 54 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/7 |
| sustainable_development_goals[0].score | 0.8500000238418579 |
| sustainable_development_goals[0].display_name | Affordable and clean energy |
| citation_normalized_percentile |