High Throughput Multi-Channel Parallelized Diffraction Convolutional Neural Network Accelerator Article Swipe
Zibo Hu
,
Shurui Li
,
Russell L. T. Schwartz
,
Maria Solyanik‐Gorgone
,
Mario Miscuglio
,
Puneet Gupta
,
Volker J. Sorger
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2112.12297
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2112.12297
Convolutional neural networks are paramount in image and signal processing including the relevant classification and training tasks alike and constitute for the majority of machine learning compute demand today. With convolution operations being computationally intensive, next generation hardware accelerators need to offer parallelization and algorithmic-hardware homomorphism. Fortunately, diffractive display optics is capable of million-channel parallel data processing at low latency, however, thus far only showed tens of Hertz slow single image and kernel capability, thereby significantly underdelivering from its performance potential. Here, we demonstrate an operation-parallelized high-throughput Fourier optic convolutional neural network accelerator. For the first time simultaneously processing of multiple kernels in Fourier domain enabled by optical diffraction has been achieved alongside with already conventional in the field input parallelism. Additionally, we show an about one hundred times system speed up over existing optical diffraction-based processors and this demonstration rivals performance of modern electronic solutions. Therefore, this system is capable of processing large-scale matrices about ten times faster than state of art electronic systems.
Related Topics
Concepts
Computer science
Convolutional neural network
Kernel (algebra)
Throughput
Convolution (computer science)
Parallel computing
Image processing
Parallel processing
Artificial neural network
Computer engineering
Computer hardware
Artificial intelligence
Image (mathematics)
Telecommunications
Wireless
Combinatorics
Mathematics
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2112.12297
- https://arxiv.org/pdf/2112.12297
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4285030483
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4285030483Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2112.12297Digital Object Identifier
- Title
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High Throughput Multi-Channel Parallelized Diffraction Convolutional Neural Network AcceleratorWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-12-23Full publication date if available
- Authors
-
Zibo Hu, Shurui Li, Russell L. T. Schwartz, Maria Solyanik‐Gorgone, Mario Miscuglio, Puneet Gupta, Volker J. SorgerList of authors in order
- Landing page
-
https://arxiv.org/abs/2112.12297Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2112.12297Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2112.12297Direct OA link when available
- Concepts
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Computer science, Convolutional neural network, Kernel (algebra), Throughput, Convolution (computer science), Parallel computing, Image processing, Parallel processing, Artificial neural network, Computer engineering, Computer hardware, Artificial intelligence, Image (mathematics), Telecommunications, Wireless, Combinatorics, MathematicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.Here, | 81 |
| abstract_inverted_index.Hertz | 67 |
| abstract_inverted_index.about | 125, 155 |
| abstract_inverted_index.alike | 17 |
| abstract_inverted_index.being | 32 |
| abstract_inverted_index.field | 118 |
| abstract_inverted_index.first | 95 |
| abstract_inverted_index.image | 6, 70 |
| abstract_inverted_index.input | 119 |
| abstract_inverted_index.offer | 41 |
| abstract_inverted_index.optic | 88 |
| abstract_inverted_index.speed | 130 |
| abstract_inverted_index.state | 160 |
| abstract_inverted_index.tasks | 16 |
| abstract_inverted_index.times | 128, 157 |
| abstract_inverted_index.demand | 27 |
| abstract_inverted_index.domain | 104 |
| abstract_inverted_index.faster | 158 |
| abstract_inverted_index.kernel | 72 |
| abstract_inverted_index.modern | 143 |
| abstract_inverted_index.neural | 1, 90 |
| abstract_inverted_index.optics | 49 |
| abstract_inverted_index.rivals | 140 |
| abstract_inverted_index.showed | 64 |
| abstract_inverted_index.signal | 8 |
| abstract_inverted_index.single | 69 |
| abstract_inverted_index.system | 129, 148 |
| abstract_inverted_index.today. | 28 |
| abstract_inverted_index.Fourier | 87, 103 |
| abstract_inverted_index.already | 114 |
| abstract_inverted_index.capable | 51, 150 |
| abstract_inverted_index.compute | 26 |
| abstract_inverted_index.display | 48 |
| abstract_inverted_index.enabled | 105 |
| abstract_inverted_index.hundred | 127 |
| abstract_inverted_index.kernels | 101 |
| abstract_inverted_index.machine | 24 |
| abstract_inverted_index.network | 91 |
| abstract_inverted_index.optical | 107, 134 |
| abstract_inverted_index.thereby | 74 |
| abstract_inverted_index.achieved | 111 |
| abstract_inverted_index.existing | 133 |
| abstract_inverted_index.hardware | 37 |
| abstract_inverted_index.however, | 60 |
| abstract_inverted_index.latency, | 59 |
| abstract_inverted_index.learning | 25 |
| abstract_inverted_index.majority | 22 |
| abstract_inverted_index.matrices | 154 |
| abstract_inverted_index.multiple | 100 |
| abstract_inverted_index.networks | 2 |
| abstract_inverted_index.parallel | 54 |
| abstract_inverted_index.relevant | 12 |
| abstract_inverted_index.systems. | 164 |
| abstract_inverted_index.training | 15 |
| abstract_inverted_index.alongside | 112 |
| abstract_inverted_index.including | 10 |
| abstract_inverted_index.paramount | 4 |
| abstract_inverted_index.Therefore, | 146 |
| abstract_inverted_index.constitute | 19 |
| abstract_inverted_index.electronic | 144, 163 |
| abstract_inverted_index.generation | 36 |
| abstract_inverted_index.intensive, | 34 |
| abstract_inverted_index.operations | 31 |
| abstract_inverted_index.potential. | 80 |
| abstract_inverted_index.processing | 9, 56, 98, 152 |
| abstract_inverted_index.processors | 136 |
| abstract_inverted_index.solutions. | 145 |
| abstract_inverted_index.capability, | 73 |
| abstract_inverted_index.convolution | 30 |
| abstract_inverted_index.demonstrate | 83 |
| abstract_inverted_index.diffraction | 108 |
| abstract_inverted_index.diffractive | 47 |
| abstract_inverted_index.large-scale | 153 |
| abstract_inverted_index.performance | 79, 141 |
| abstract_inverted_index.Fortunately, | 46 |
| abstract_inverted_index.accelerator. | 92 |
| abstract_inverted_index.accelerators | 38 |
| abstract_inverted_index.conventional | 115 |
| abstract_inverted_index.parallelism. | 120 |
| abstract_inverted_index.Additionally, | 121 |
| abstract_inverted_index.Convolutional | 0 |
| abstract_inverted_index.convolutional | 89 |
| abstract_inverted_index.demonstration | 139 |
| abstract_inverted_index.homomorphism. | 45 |
| abstract_inverted_index.significantly | 75 |
| abstract_inverted_index.classification | 13 |
| abstract_inverted_index.simultaneously | 97 |
| abstract_inverted_index.computationally | 33 |
| abstract_inverted_index.high-throughput | 86 |
| abstract_inverted_index.million-channel | 53 |
| abstract_inverted_index.parallelization | 42 |
| abstract_inverted_index.underdelivering | 76 |
| abstract_inverted_index.diffraction-based | 135 |
| abstract_inverted_index.algorithmic-hardware | 44 |
| abstract_inverted_index.operation-parallelized | 85 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.5099999904632568 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile |