Hardware implementation of photonic neuromorphic autonomous navigation
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arXiv
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| Autores principales: | , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
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| _version_ | 1866908802532507648 |
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| author | Chen, Yonghang Xiang, Shuiying Zeng, Xintao Yu, Mengting Zou, Tao Shi, Shangxuan Guo, Xingxing Han, Yanan Zhang, Yahui Hao, Yue |
| author_facet | Chen, Yonghang Xiang, Shuiying Zeng, Xintao Yu, Mengting Zou, Tao Shi, Shangxuan Guo, Xingxing Han, Yanan Zhang, Yahui Hao, Yue |
| contents | Reinforcement learning (RL) is a core technology enabling the transition of artificial intelligence (AI) from perception to decision-making, but its deployment on conventional electronic hardware suffers from high latency and energy consumption imposed by the von Neumann architecture. Here, we propose a photonic spiking twin delayed deep deterministic policy gradient (TD3) reinforcement learning architecture for neuromorphic autonomous navigation and experimentally validate it on a distributed feedback laser with a saturable absorber (DFB-SA) array. The hybrid architecture integrates a photonic spiking Actor network with dual continuous-valued Critic networks, where the final nonlinear spiking activation layer of the Actor is deployed on the DFB-SA laser array. In autonomous navigation tasks, the system achieves an average reward of 58.22 plus-minus 17.29 and a success rate of 80% plus-minus 8.3%. Hardware-software co-inference demonstrates an estimated energy consumption of 0.78 nJ/inf and an ultra-low latency of 191.20 ps/inf, with co-inference error rates of 0.051% and 0.059% in task scenarios with and without obstacle interference, respectively. Simulations for error-activated channels show full agreement with the expected responses, validating the dynamic characteristics of the DFB-SA laser. The architecture shows strong potential for integration with large-scale photonic linear computing chips, enabling fully-functional photonic computation and low-power, low-latency neuromorphic autonomous navigation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_01079 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Hardware implementation of photonic neuromorphic autonomous navigation Chen, Yonghang Xiang, Shuiying Zeng, Xintao Yu, Mengting Zou, Tao Shi, Shangxuan Guo, Xingxing Han, Yanan Zhang, Yahui Hao, Yue Optics Reinforcement learning (RL) is a core technology enabling the transition of artificial intelligence (AI) from perception to decision-making, but its deployment on conventional electronic hardware suffers from high latency and energy consumption imposed by the von Neumann architecture. Here, we propose a photonic spiking twin delayed deep deterministic policy gradient (TD3) reinforcement learning architecture for neuromorphic autonomous navigation and experimentally validate it on a distributed feedback laser with a saturable absorber (DFB-SA) array. The hybrid architecture integrates a photonic spiking Actor network with dual continuous-valued Critic networks, where the final nonlinear spiking activation layer of the Actor is deployed on the DFB-SA laser array. In autonomous navigation tasks, the system achieves an average reward of 58.22 plus-minus 17.29 and a success rate of 80% plus-minus 8.3%. Hardware-software co-inference demonstrates an estimated energy consumption of 0.78 nJ/inf and an ultra-low latency of 191.20 ps/inf, with co-inference error rates of 0.051% and 0.059% in task scenarios with and without obstacle interference, respectively. Simulations for error-activated channels show full agreement with the expected responses, validating the dynamic characteristics of the DFB-SA laser. The architecture shows strong potential for integration with large-scale photonic linear computing chips, enabling fully-functional photonic computation and low-power, low-latency neuromorphic autonomous navigation. |
| title | Hardware implementation of photonic neuromorphic autonomous navigation |
| topic | Optics |
| url | https://arxiv.org/abs/2602.01079 |