High Clockrate Free-space Optical In-Memory Computing
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866918147101032448 |
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| author | Liang, Yuanhao Wang, James Xue, Kaiwen Ren, Xinyi Yin, Ran Ou, Shaoyuan Zhou, Lian Li, Yuan Heuser, Tobias Heermeier, Niels Christen, Ian Lott, James A. Reitzenstein, Stephan Yu, Mengjie Chen, Zaijun |
| author_facet | Liang, Yuanhao Wang, James Xue, Kaiwen Ren, Xinyi Yin, Ran Ou, Shaoyuan Zhou, Lian Li, Yuan Heuser, Tobias Heermeier, Niels Christen, Ian Lott, James A. Reitzenstein, Stephan Yu, Mengjie Chen, Zaijun |
| contents | The ability to process and act on data in real time is increasingly critical for applications ranging from autonomous vehicles, three-dimensional environmental sensing and remote robotics. However, the deployment of deep neural networks (DNNs) in edge devices is hindered by the lack of energy-efficient scalable computing hardware. Here, we introduce a fanout spatial time-of-flight optical neural network (FAST-ONN) that calculates billions of convolutions per second with ultralow latency and power consumption. This is enabled by the combination of high-speed dense arrays of vertical-cavity surface-emitting lasers (VCSELs) for input modulation with spatial light modulators of high pixel counts for in-memory weighting. In a three-dimensional optical system, parallel differential readout allows signed weight values accurate inference in a single shot. The performance is benchmarked with feature extraction in You-Only-Look-Once (YOLO) for convolution at 100 million frames per second (MFPS), and in-system backward propagation training with photonic reprogrammability. The VCSEL transmitters are implementable in any free-space optical computing systems to improve the clockrate to over gigahertz. The high scalability in device counts and channel parallelism enables a new avenue to scale up free space computing hardware. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_19642 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | High Clockrate Free-space Optical In-Memory Computing Liang, Yuanhao Wang, James Xue, Kaiwen Ren, Xinyi Yin, Ran Ou, Shaoyuan Zhou, Lian Li, Yuan Heuser, Tobias Heermeier, Niels Christen, Ian Lott, James A. Reitzenstein, Stephan Yu, Mengjie Chen, Zaijun Emerging Technologies Optics The ability to process and act on data in real time is increasingly critical for applications ranging from autonomous vehicles, three-dimensional environmental sensing and remote robotics. However, the deployment of deep neural networks (DNNs) in edge devices is hindered by the lack of energy-efficient scalable computing hardware. Here, we introduce a fanout spatial time-of-flight optical neural network (FAST-ONN) that calculates billions of convolutions per second with ultralow latency and power consumption. This is enabled by the combination of high-speed dense arrays of vertical-cavity surface-emitting lasers (VCSELs) for input modulation with spatial light modulators of high pixel counts for in-memory weighting. In a three-dimensional optical system, parallel differential readout allows signed weight values accurate inference in a single shot. The performance is benchmarked with feature extraction in You-Only-Look-Once (YOLO) for convolution at 100 million frames per second (MFPS), and in-system backward propagation training with photonic reprogrammability. The VCSEL transmitters are implementable in any free-space optical computing systems to improve the clockrate to over gigahertz. The high scalability in device counts and channel parallelism enables a new avenue to scale up free space computing hardware. |
| title | High Clockrate Free-space Optical In-Memory Computing |
| topic | Emerging Technologies Optics |
| url | https://arxiv.org/abs/2509.19642 |