In‐Sensor Reservoir Computing Based on Optoelectronic Synapse
Conventional machine vision systems suffer from great data latency and energy consumption in cognitive tasks due to the separated vision sensors, memory units, and processors. In‐sensor computing based on optoelectronic synapses allows efficient computation by directly sensing and processing optical...
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-01-01
|
| Series: | Advanced Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/aisy.202200196 |
| _version_ | 1797946294493249536 |
|---|---|
| author | Yi Sun Qingjiang Li Xi Zhu Cen Liao Yongzhou Wang Zhiwei Li Sen Liu Hui Xu Wei Wang |
| author_facet | Yi Sun Qingjiang Li Xi Zhu Cen Liao Yongzhou Wang Zhiwei Li Sen Liu Hui Xu Wei Wang |
| author_sort | Yi Sun |
| collection | DOAJ |
| description | Conventional machine vision systems suffer from great data latency and energy consumption in cognitive tasks due to the separated vision sensors, memory units, and processors. In‐sensor computing based on optoelectronic synapses allows efficient computation by directly sensing and processing optical signals. Herein, an optoelectronic synapse based on Au/ZnO:N/IGZO/TiN structure is proposed. It shows uniform optical SET and electrical RESET behaviors, with various light‐tunable plasticity. Furthermore, a 4‐bit reservoir is experimentally implemented on the device, which is ideal to construct in‐sensor reservoir computing (RC) system. By converting spatiotemporal optical signals to higher dimensional feature space, in‐sensor RC has a great advantage in processing sequential visual information. Simulation results demonstrate that the in‐sensor RC system based on the proposed synapse achieves a considerable recognition accuracy (90.45%) for the MNIST dataset with very limited 36‐30‐10 perceptron network, and a 97.14% accuracy for human action classification from sequential vision data based on the Weizmann dataset. This work proves the low training cost and great efficiency for processing spatiotemporal and sequential optical signals, which may pave a new way for future machine vision applications. |
| first_indexed | 2024-04-10T21:08:46Z |
| format | Article |
| id | doaj.art-65760f76c1154599ab3419761d26bb75 |
| institution | Directory Open Access Journal |
| issn | 2640-4567 |
| language | English |
| last_indexed | 2024-04-10T21:08:46Z |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Intelligent Systems |
| spelling | doaj.art-65760f76c1154599ab3419761d26bb752023-01-21T05:53:25ZengWileyAdvanced Intelligent Systems2640-45672023-01-0151n/an/a10.1002/aisy.202200196In‐Sensor Reservoir Computing Based on Optoelectronic SynapseYi Sun0Qingjiang Li1Xi Zhu2Cen Liao3Yongzhou Wang4Zhiwei Li5Sen Liu6Hui Xu7Wei Wang8College of Electronic Science and Technology National University of Defense Technology Changsha 410073 ChinaCollege of Electronic Science and Technology National University of Defense Technology Changsha 410073 ChinaCollege of Electronic Science and Technology National University of Defense Technology Changsha 410073 ChinaCollege of Electronic Science and Technology National University of Defense Technology Changsha 410073 ChinaCollege of Electronic Science and Technology National University of Defense Technology Changsha 410073 ChinaCollege of Electronic Science and Technology National University of Defense Technology Changsha 410073 ChinaCollege of Electronic Science and Technology National University of Defense Technology Changsha 410073 ChinaCollege of Electronic Science and Technology National University of Defense Technology Changsha 410073 ChinaCollege of Electronic Science and Technology National University of Defense Technology Changsha 410073 ChinaConventional machine vision systems suffer from great data latency and energy consumption in cognitive tasks due to the separated vision sensors, memory units, and processors. In‐sensor computing based on optoelectronic synapses allows efficient computation by directly sensing and processing optical signals. Herein, an optoelectronic synapse based on Au/ZnO:N/IGZO/TiN structure is proposed. It shows uniform optical SET and electrical RESET behaviors, with various light‐tunable plasticity. Furthermore, a 4‐bit reservoir is experimentally implemented on the device, which is ideal to construct in‐sensor reservoir computing (RC) system. By converting spatiotemporal optical signals to higher dimensional feature space, in‐sensor RC has a great advantage in processing sequential visual information. Simulation results demonstrate that the in‐sensor RC system based on the proposed synapse achieves a considerable recognition accuracy (90.45%) for the MNIST dataset with very limited 36‐30‐10 perceptron network, and a 97.14% accuracy for human action classification from sequential vision data based on the Weizmann dataset. This work proves the low training cost and great efficiency for processing spatiotemporal and sequential optical signals, which may pave a new way for future machine vision applications.https://doi.org/10.1002/aisy.202200196action classificationin-sensor computingoptoelectronic synapsereservoir |
| spellingShingle | Yi Sun Qingjiang Li Xi Zhu Cen Liao Yongzhou Wang Zhiwei Li Sen Liu Hui Xu Wei Wang In‐Sensor Reservoir Computing Based on Optoelectronic Synapse Advanced Intelligent Systems action classification in-sensor computing optoelectronic synapse reservoir |
| title | In‐Sensor Reservoir Computing Based on Optoelectronic Synapse |
| title_full | In‐Sensor Reservoir Computing Based on Optoelectronic Synapse |
| title_fullStr | In‐Sensor Reservoir Computing Based on Optoelectronic Synapse |
| title_full_unstemmed | In‐Sensor Reservoir Computing Based on Optoelectronic Synapse |
| title_short | In‐Sensor Reservoir Computing Based on Optoelectronic Synapse |
| title_sort | in sensor reservoir computing based on optoelectronic synapse |
| topic | action classification in-sensor computing optoelectronic synapse reservoir |
| url | https://doi.org/10.1002/aisy.202200196 |
| work_keys_str_mv | AT yisun insensorreservoircomputingbasedonoptoelectronicsynapse AT qingjiangli insensorreservoircomputingbasedonoptoelectronicsynapse AT xizhu insensorreservoircomputingbasedonoptoelectronicsynapse AT cenliao insensorreservoircomputingbasedonoptoelectronicsynapse AT yongzhouwang insensorreservoircomputingbasedonoptoelectronicsynapse AT zhiweili insensorreservoircomputingbasedonoptoelectronicsynapse AT senliu insensorreservoircomputingbasedonoptoelectronicsynapse AT huixu insensorreservoircomputingbasedonoptoelectronicsynapse AT weiwang insensorreservoircomputingbasedonoptoelectronicsynapse |