Photonic neural field on a silicon chip: large-scale, high-speed neuro-inspired computing and sensing Article Swipe
Satoshi Sunada
,
Atsushi Uchida
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1364/optica.434918
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1364/optica.434918
Photonic neural networks have significant potential for high-speed neural processing with low latency and ultralow energy consumption. However, the on-chip implementation of a large-scale neural network is still challenging owing to its low scalability. Herein, we propose the concept of a photonic neural field and implement it experimentally on a silicon chip to realize highly scalable neuro-inspired computing. In contrast to existing photonic neural networks, the photonic neural field is a spatially continuous field that nonlinearly responds to optical inputs, and its high spatial degrees of freedom allow for large-scale and high-density neural processing on a millimeter-scale chip. In this study, we use the on-chip photonic neural field as a reservoir of information and demonstrate a high-speed chaotic time-series prediction with low errors using a training approach similar to reservoir computing. We show that the photonic neural field is potentially capable of executing more than one peta multiply–accumulate operations per second for a single input wavelength on a footprint as small as a few square millimeters. The operation of the neural field is energy efficient due to a passive scattering process, for which the required power comes only from the optical input. We also show that in addition to processing, the photonic neural field can be used for rapidly sensing the temporal variation of an optical phase, facilitated by its high sensitivity to optical inputs. The merging of optical processing with optical sensing paves the way for an end-to-end data-driven optical sensing scheme.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1364/optica.434918
- OA Status
- gold
- Cited By
- 54
- References
- 72
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3202971499
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3202971499Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1364/optica.434918Digital Object Identifier
- Title
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Photonic neural field on a silicon chip: large-scale, high-speed neuro-inspired computing and sensingWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-10-05Full publication date if available
- Authors
-
Satoshi Sunada, Atsushi UchidaList of authors in order
- Landing page
-
https://doi.org/10.1364/optica.434918Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1364/optica.434918Direct OA link when available
- Concepts
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Photonics, Artificial neural network, Computer science, Reservoir computing, Scalability, Silicon photonics, Chip, Electronic engineering, Recurrent neural network, Artificial intelligence, Telecommunications, Physics, Optoelectronics, Engineering, DatabaseTop concepts (fields/topics) attached by OpenAlex
- Cited by
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54Total citation count in OpenAlex
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-
2025: 12, 2024: 15, 2023: 17, 2022: 10Per-year citation counts (last 5 years)
- References (count)
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72Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2513148968, https://openalex.org/W1984020445, https://openalex.org/W2138913040, https://openalex.org/W3046016807, https://openalex.org/W3013519636, https://openalex.org/W3096230432, https://openalex.org/W3044091938, https://openalex.org/W2887258823, https://openalex.org/W2789326804, https://openalex.org/W2772397789, https://openalex.org/W1977664984, https://openalex.org/W2739588406, https://openalex.org/W2904694042, https://openalex.org/W2798701005, https://openalex.org/W3016532901, https://openalex.org/W3003967059, https://openalex.org/W3118265437, https://openalex.org/W2976897596, https://openalex.org/W3094052952, https://openalex.org/W2752849906, https://openalex.org/W2053297984, https://openalex.org/W2007328446, https://openalex.org/W2613833839, https://openalex.org/W2113132555, https://openalex.org/W1965702053, https://openalex.org/W2029939668, https://openalex.org/W2063782756, https://openalex.org/W2256527444, https://openalex.org/W2170993585, https://openalex.org/W2587524409, https://openalex.org/W2898551707, https://openalex.org/W2996409363, https://openalex.org/W3008334675, https://openalex.org/W2955431988, https://openalex.org/W3086955246, https://openalex.org/W2975126970, https://openalex.org/W3002979339, https://openalex.org/W2953401808, https://openalex.org/W3126304295, https://openalex.org/W3119901359, https://openalex.org/W2118706537, https://openalex.org/W2103179919, https://openalex.org/W2036451492, https://openalex.org/W2986967681, https://openalex.org/W3196262621, https://openalex.org/W3153257371, https://openalex.org/W2079329690, https://openalex.org/W2073284918, https://openalex.org/W2467462666, https://openalex.org/W2158283630, https://openalex.org/W2789580836, https://openalex.org/W2974263490, https://openalex.org/W3006951342, https://openalex.org/W3047302674, https://openalex.org/W3113152461, https://openalex.org/W1976003027, https://openalex.org/W2015329309, https://openalex.org/W1991402014, https://openalex.org/W2963941964, https://openalex.org/W3104086227, https://openalex.org/W3098622544, https://openalex.org/W3128451613, https://openalex.org/W3102572122, https://openalex.org/W3151618053, https://openalex.org/W3099119964, https://openalex.org/W3187692234, https://openalex.org/W2996678389, https://openalex.org/W3099852670, https://openalex.org/W2995728393, https://openalex.org/W3101465594, https://openalex.org/W3120165331, https://openalex.org/W3118548495 |
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