High‐Throughput Multichannel 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?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1002/lpor.202200213
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1002/lpor.202200213
Convolutional neural networks are paramount in image and signal processing, and are responsible for the majority of image recognition power consumption today, concentrated mainly in convolution computations. With convolution operations being computationally intensive, next‐generation hardware accelerators need to offer parallelization and high efficiency. Diffractive optics offers the promise of low‐latency, highly parallel convolution operations. However, thus far parallelism is only partially harvested, thereby significantly underdelivering in comparison to its throughput potential. Here, a parallelized operation high‐throughput Fourier optic convolutional accelerator is demonstrated. For the first time, simultaneous processing of multiple kernels in Fourier domain enabled by optical diffraction orders is achieved alongside input parallelism. The proposed approach can offer ≈100× speedup over the previous generation optical diffraction‐based processor and 10× speedup over other state‐of‐the‐art optical Fourier classifiers.
Related Topics
Concepts
Speedup
Computer science
Convolution (computer science)
Convolutional neural network
Throughput
Parallel computing
Computation
Fourier transform
Diffraction
Parallel processing
Fast Fourier transform
Computational science
Algorithm
Artificial intelligence
Optics
Artificial neural network
Telecommunications
Physics
Quantum mechanics
Wireless
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1002/lpor.202200213
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/lpor.202200213
- OA Status
- hybrid
- Cited By
- 15
- References
- 37
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4300716945
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4300716945Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1002/lpor.202200213Digital Object Identifier
- Title
-
High‐Throughput Multichannel Parallelized Diffraction Convolutional Neural Network AcceleratorWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-10-03Full 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://doi.org/10.1002/lpor.202200213Publisher landing page
- PDF URL
-
https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/lpor.202200213Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/lpor.202200213Direct OA link when available
- Concepts
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Speedup, Computer science, Convolution (computer science), Convolutional neural network, Throughput, Parallel computing, Computation, Fourier transform, Diffraction, Parallel processing, Fast Fourier transform, Computational science, Algorithm, Artificial intelligence, Optics, Artificial neural network, Telecommunications, Physics, Quantum mechanics, WirelessTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
15Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 5, 2024: 5, 2023: 5Per-year citation counts (last 5 years)
- References (count)
-
37Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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