Channel Tiling for Improved Performance and Accuracy of Optical Neural Network Accelerators Article Swipe
Shurui Li
,
Mario Miscuglio
,
Volker J. Sorger
,
Puneet Gupta
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2011.07391
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2011.07391
Low latency, high throughput inference on Convolution Neural Networks (CNNs) remains a challenge, especially for applications requiring large input or large kernel sizes. 4F optics provides a solution to accelerate CNNs by converting convolutions into Fourier-domain point-wise multiplications that are computationally 'free' in optical domain. However, existing 4F CNN systems suffer from the all-positive sensor readout issue which makes the implementation of a multi-channel, multi-layer CNN not scalable or even impractical. In this paper we propose a simple channel tiling scheme for 4F CNN systems that utilizes the high resolution of 4F system to perform channel summation inherently in optical domain before sensor detection, so the outputs of different channels can be correctly accumulated. Compared to state of the art, channel tiling gives similar accuracy, significantly better robustness to sensing quantization (33\% improvement in required sensing precision) error and noise (10dB reduction in tolerable sensing noise), 0.5X total filters required, 10-50X+ throughput improvement and as much as 3X reduction in required output camera resolution/bandwidth. Not requiring any additional optical hardware, the proposed channel tiling approach addresses an important throughput and precision bottleneck of high-speed, massively-parallel optical 4F computing systems.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2011.07391
- https://arxiv.org/pdf/2011.07391
- OA Status
- green
- Cited By
- 5
- References
- 34
- Related Works
- 10
- OpenAlex ID
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https://doi.org/10.48550/arxiv.2011.07391Digital Object Identifier
- Title
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Channel Tiling for Improved Performance and Accuracy of Optical Neural Network AcceleratorsWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
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2020Year of publication
- Publication date
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2020-11-14Full publication date if available
- Authors
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Shurui Li, Mario Miscuglio, Volker J. Sorger, Puneet GuptaList of authors in order
- Landing page
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https://arxiv.org/abs/2011.07391Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2011.07391Direct link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2011.07391Direct OA link when available
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Channel (broadcasting), Computer science, Artificial neural network, Computer network, Artificial intelligenceTop concepts (fields/topics) attached by OpenAlex
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5Total citation count in OpenAlex
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2024: 1, 2023: 2, 2022: 1, 2021: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.hardware, | 169 |
| abstract_inverted_index.important | 177 |
| abstract_inverted_index.inference | 4 |
| abstract_inverted_index.precision | 180 |
| abstract_inverted_index.reduction | 141, 158 |
| abstract_inverted_index.required, | 149 |
| abstract_inverted_index.requiring | 16, 165 |
| abstract_inverted_index.summation | 96 |
| abstract_inverted_index.tolerable | 143 |
| abstract_inverted_index.accelerate | 29 |
| abstract_inverted_index.additional | 167 |
| abstract_inverted_index.bottleneck | 181 |
| abstract_inverted_index.challenge, | 12 |
| abstract_inverted_index.converting | 32 |
| abstract_inverted_index.detection, | 103 |
| abstract_inverted_index.especially | 13 |
| abstract_inverted_index.inherently | 97 |
| abstract_inverted_index.point-wise | 36 |
| abstract_inverted_index.precision) | 136 |
| abstract_inverted_index.resolution | 89 |
| abstract_inverted_index.robustness | 127 |
| abstract_inverted_index.throughput | 3, 151, 178 |
| abstract_inverted_index.Convolution | 6 |
| abstract_inverted_index.high-speed, | 183 |
| abstract_inverted_index.improvement | 132, 152 |
| abstract_inverted_index.multi-layer | 64 |
| abstract_inverted_index.accumulated. | 113 |
| abstract_inverted_index.all-positive | 53 |
| abstract_inverted_index.applications | 15 |
| abstract_inverted_index.convolutions | 33 |
| abstract_inverted_index.impractical. | 70 |
| abstract_inverted_index.quantization | 130 |
| abstract_inverted_index.significantly | 125 |
| abstract_inverted_index.Fourier-domain | 35 |
| abstract_inverted_index.implementation | 60 |
| abstract_inverted_index.multi-channel, | 63 |
| abstract_inverted_index.computationally | 40 |
| abstract_inverted_index.multiplications | 37 |
| abstract_inverted_index.massively-parallel | 184 |
| abstract_inverted_index.resolution/bandwidth. | 163 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/7 |
| sustainable_development_goals[0].score | 0.49000000953674316 |
| sustainable_development_goals[0].display_name | Affordable and clean energy |
| citation_normalized_percentile |