WDM equipped universal linear optics for programmable neuromorphic photonic processors Article Swipe
Angelina Totović
,
Christos Pappas
,
Manos Kirtas
,
Apostolos Tsakyridis
,
George Giamougiannis
,
Nikolaos Passalis
,
Miltiadis Moralis‐Pegios
,
Anastasios Tefas
,
Nikos Pleros
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1088/2634-4386/ac724d
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1088/2634-4386/ac724d
Non-von-Neumann computing architectures and deep learning training models have sparked a new computational era where neurons are forming the main architectural backbone and vector, matrix and tensor multiplications comprise the basic mathematical toolbox. This paradigm shift has triggered a new race among hardware technology candidates; within this frame, the field of neuromorphic photonics promises to convolve the targeted algebraic portfolio along a computational circuitry with unique speed, parallelization, and energy efficiency advantages. Fueled by the inherent energy efficient analog matrix multiply operations of optics, the staggering advances of photonic integration and the enhanced multiplexing degrees offered by light, neuromorphic photonics has stamped the resurgence of optical computing brining a unique perspective in low-energy and ultra-fast linear algebra functions. However, the field of neuromorphic photonics has relied so far on two basic architectural schemes, i.e., coherent linear optical circuits and incoherent WDM approaches, where wavelengths have still not been exploited as a new mathematical dimension. In this paper, we present a radically new approach for promoting the synergy of WDM with universal linear optics and demonstrate a new, high-fidelity crossbar-based neuromorphic photonic platform, able to support matmul with multidimensional operands. Going a step further, we introduce the concept of programmable input and weight banks, supporting in situ reconfigurability, forming in this way the first WDM-equipped universal linear optical operator and demonstrating different operational modes like matrix-by-matrix and vector-by-tensor multiplication. The benefits of our platform are highlighted in a fully convolutional neural network layout that is responsible for parity identification in the MNIST handwritten digit dataset, with physical layer simulations revealing an accuracy of ∼94%, degraded by only 2% compared to respective results obtained when executed entirely by software. Finally, our in-depth analysis provides the guidelines for neuromorphic photonic processor performance improvement, revealing along the way that 4 bit quantization is sufficient for inputs, whereas the weights can be implemented with as low as 2 bits of precision, offering substantial benefits in terms of driving circuitry complexity and energy savings.
Related Topics
Concepts
Neuromorphic engineering
Reconfigurability
Computer science
Photonics
Multiplexing
Matrix multiplication
Efficient energy use
Computer architecture
Electronic engineering
Artificial neural network
Telecommunications
Physics
Artificial intelligence
Engineering
Optics
Electrical engineering
Quantum
Quantum mechanics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1088/2634-4386/ac724d
- OA Status
- gold
- Cited By
- 32
- References
- 59
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4281384453
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4281384453Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1088/2634-4386/ac724dDigital Object Identifier
- Title
-
WDM equipped universal linear optics for programmable neuromorphic photonic processorsWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-05-23Full publication date if available
- Authors
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Angelina Totović, Christos Pappas, Manos Kirtas, Apostolos Tsakyridis, George Giamougiannis, Nikolaos Passalis, Miltiadis Moralis‐Pegios, Anastasios Tefas, Nikos PlerosList of authors in order
- Landing page
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https://doi.org/10.1088/2634-4386/ac724dPublisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1088/2634-4386/ac724dDirect OA link when available
- Concepts
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Neuromorphic engineering, Reconfigurability, Computer science, Photonics, Multiplexing, Matrix multiplication, Efficient energy use, Computer architecture, Electronic engineering, Artificial neural network, Telecommunications, Physics, Artificial intelligence, Engineering, Optics, Electrical engineering, Quantum, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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32Total citation count in OpenAlex
- Citations by year (recent)
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2025: 8, 2024: 7, 2023: 10, 2022: 7Per-year citation counts (last 5 years)
- References (count)
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59Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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