Monolithically integrated asynchronous optical recurrent accelerator
Abstract Computing with light is widely recognized as a promising paradigm for overcoming the energy and latency limitations of electronic computing. However, the energy consumption and latency in current optical computing hardware predominantly arise in the electrical domain rather than the optical...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-05-01
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| Series: | eLight |
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| Online Access: | https://doi.org/10.1186/s43593-025-00084-y |
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| author | Bo Wu Haojun Zhou Junwei Cheng Wenkai Zhang Shiji Zhang Chaoran Huang Dongmei Huang Hailong Zhou Jianji Dong Xinliang Zhang |
| author_facet | Bo Wu Haojun Zhou Junwei Cheng Wenkai Zhang Shiji Zhang Chaoran Huang Dongmei Huang Hailong Zhou Jianji Dong Xinliang Zhang |
| author_sort | Bo Wu |
| collection | DOAJ |
| description | Abstract Computing with light is widely recognized as a promising paradigm for overcoming the energy and latency limitations of electronic computing. However, the energy consumption and latency in current optical computing hardware predominantly arise in the electrical domain rather than the optical domain, primarily due to frequent signal conversions between optical (analog) and electrical (digital) formats. Furthermore, as the operating frequency of optical computing surpasses the GHz range, the synchronization of parallel electrical signals and the management of optical delays become increasingly critical. These challenges exacerbate energy consumption and latency, particularly in recurrent optical operations. To address these limitations, we propose a novel asynchronous computing paradigm for on-chip optical recurrent accelerators based on wavelength encoding, effectively mitigating synchronization challenges. By leveraging the intrinsic causality of wavelength relay, our approach eliminates the need for rigorous temporal alignment. To demonstrate the flexibility and efficacy of this asynchronous paradigm, we present two advanced recurrent models—an optical hidden Markov model and an optical recurrent neural network—monolithically integrated for the first time. These models incorporate hundreds of linear and nonlinear computing units densely packed into a compact footprint of just 10 mm2. Experimental evaluations on various benchmark tasks underscore the superior energy efficiency and low latency of the proposed asynchronous optical accelerators. This innovation enables the efficient processing of large-scale parallel signals and positions optical processors as a pivotal technology for applications such as autonomous driving and intelligent robotics. |
| format | Article |
| id | doaj-art-c107b8fc8b7d4f8d8b681e28f2b94cfc |
| institution | DOAJ |
| issn | 2097-1710 2662-8643 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | eLight |
| spelling | doaj-art-c107b8fc8b7d4f8d8b681e28f2b94cfc2025-08-20T03:09:34ZengSpringerOpeneLight2097-17102662-86432025-05-015111410.1186/s43593-025-00084-yMonolithically integrated asynchronous optical recurrent acceleratorBo Wu0Haojun Zhou1Junwei Cheng2Wenkai Zhang3Shiji Zhang4Chaoran Huang5Dongmei Huang6Hailong Zhou7Jianji Dong8Xinliang Zhang9Wuhan National Laboratory for Optoelectronics, School of Optical and Electronic Information, Huazhong University of Science and TechnologyWuhan National Laboratory for Optoelectronics, School of Optical and Electronic Information, Huazhong University of Science and TechnologyWuhan National Laboratory for Optoelectronics, School of Optical and Electronic Information, Huazhong University of Science and TechnologyWuhan National Laboratory for Optoelectronics, School of Optical and Electronic Information, Huazhong University of Science and TechnologyWuhan National Laboratory for Optoelectronics, School of Optical and Electronic Information, Huazhong University of Science and TechnologyDepartment of Electronic Engineering, The Chinese University of Hong KongPhotonics Research Institute, Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic UniversityWuhan National Laboratory for Optoelectronics, School of Optical and Electronic Information, Huazhong University of Science and TechnologyWuhan National Laboratory for Optoelectronics, School of Optical and Electronic Information, Huazhong University of Science and TechnologyWuhan National Laboratory for Optoelectronics, School of Optical and Electronic Information, Huazhong University of Science and TechnologyAbstract Computing with light is widely recognized as a promising paradigm for overcoming the energy and latency limitations of electronic computing. However, the energy consumption and latency in current optical computing hardware predominantly arise in the electrical domain rather than the optical domain, primarily due to frequent signal conversions between optical (analog) and electrical (digital) formats. Furthermore, as the operating frequency of optical computing surpasses the GHz range, the synchronization of parallel electrical signals and the management of optical delays become increasingly critical. These challenges exacerbate energy consumption and latency, particularly in recurrent optical operations. To address these limitations, we propose a novel asynchronous computing paradigm for on-chip optical recurrent accelerators based on wavelength encoding, effectively mitigating synchronization challenges. By leveraging the intrinsic causality of wavelength relay, our approach eliminates the need for rigorous temporal alignment. To demonstrate the flexibility and efficacy of this asynchronous paradigm, we present two advanced recurrent models—an optical hidden Markov model and an optical recurrent neural network—monolithically integrated for the first time. These models incorporate hundreds of linear and nonlinear computing units densely packed into a compact footprint of just 10 mm2. Experimental evaluations on various benchmark tasks underscore the superior energy efficiency and low latency of the proposed asynchronous optical accelerators. This innovation enables the efficient processing of large-scale parallel signals and positions optical processors as a pivotal technology for applications such as autonomous driving and intelligent robotics.https://doi.org/10.1186/s43593-025-00084-yAsynchronous operationOptical recurrent acceleratorOptical hidden Markov modelOptical recurrent neural network |
| spellingShingle | Bo Wu Haojun Zhou Junwei Cheng Wenkai Zhang Shiji Zhang Chaoran Huang Dongmei Huang Hailong Zhou Jianji Dong Xinliang Zhang Monolithically integrated asynchronous optical recurrent accelerator eLight Asynchronous operation Optical recurrent accelerator Optical hidden Markov model Optical recurrent neural network |
| title | Monolithically integrated asynchronous optical recurrent accelerator |
| title_full | Monolithically integrated asynchronous optical recurrent accelerator |
| title_fullStr | Monolithically integrated asynchronous optical recurrent accelerator |
| title_full_unstemmed | Monolithically integrated asynchronous optical recurrent accelerator |
| title_short | Monolithically integrated asynchronous optical recurrent accelerator |
| title_sort | monolithically integrated asynchronous optical recurrent accelerator |
| topic | Asynchronous operation Optical recurrent accelerator Optical hidden Markov model Optical recurrent neural network |
| url | https://doi.org/10.1186/s43593-025-00084-y |
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