Low-latency neuromorphic air hockey player
Brains process sensory information to guide behaviour, enabling organisms to adapt to dynamic and unpredictable conditions. Neuromorphic engineering seeks to emulate these neurobiological principles to develop compact, low-power systems capable of real-time sensory-motor integration. This approach a...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2025-01-01
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| Series: | Neuromorphic Computing and Engineering |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/2634-4386/addc15 |
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| _version_ | 1850239637438595072 |
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| author | Juan P Romero B Dimitrios Korakovounis Jens E Pedersen Jorg Conradt |
| author_facet | Juan P Romero B Dimitrios Korakovounis Jens E Pedersen Jorg Conradt |
| author_sort | Juan P Romero B |
| collection | DOAJ |
| description | Brains process sensory information to guide behaviour, enabling organisms to adapt to dynamic and unpredictable conditions. Neuromorphic engineering seeks to emulate these neurobiological principles to develop compact, low-power systems capable of real-time sensory-motor integration. This approach addresses some limitations of traditional AI and holds promise for autonomous systems that can interact robustly with the real world. However, most of today’s widely used neuromorphic benchmarks focus primarily on improving accuracy metrics using pre-recorded datasets, often overlooking critical factors such as latency and power consumption. This underscores the need for benchmarks to evaluate real-time performance under noisy, dynamic conditions. To address this need, we developed a system that uses spiking neural networks (SNNs) to control a robotic manipulator in an air-hockey game. In this setup, the automated opponent uses SNNs to process data from an event-based camera, enabling it to track the puck’s movements and respond to the actions of a human player. Our study demonstrates the potential of SNNs to accomplish fast real-time tasks while running on massively parallel hardware. We believe our air-hockey platform provides a versatile testbed for evaluating neuromorphic systems and invites further exploration of advanced algorithms, such as those incorporating trajectory prediction or adaptive learning, which could significantly enhance real-time decision-making and control. |
| format | Article |
| id | doaj-art-ef30e8f4e42743a39a63afb74a850236 |
| institution | OA Journals |
| issn | 2634-4386 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Neuromorphic Computing and Engineering |
| spelling | doaj-art-ef30e8f4e42743a39a63afb74a8502362025-08-20T02:01:05ZengIOP PublishingNeuromorphic Computing and Engineering2634-43862025-01-015202401410.1088/2634-4386/addc15Low-latency neuromorphic air hockey playerJuan P Romero B0https://orcid.org/0009-0004-1644-2740Dimitrios Korakovounis1https://orcid.org/0009-0006-2490-3206Jens E Pedersen2https://orcid.org/0000-0001-6012-7415Jorg Conradt3https://orcid.org/0000-0001-5998-9640NCS Lab. Royal Institute of Technology , Stockholm, SwedenNCS Lab. Royal Institute of Technology , Stockholm, SwedenNCS Lab. Royal Institute of Technology , Stockholm, SwedenNCS Lab. Royal Institute of Technology , Stockholm, SwedenBrains process sensory information to guide behaviour, enabling organisms to adapt to dynamic and unpredictable conditions. Neuromorphic engineering seeks to emulate these neurobiological principles to develop compact, low-power systems capable of real-time sensory-motor integration. This approach addresses some limitations of traditional AI and holds promise for autonomous systems that can interact robustly with the real world. However, most of today’s widely used neuromorphic benchmarks focus primarily on improving accuracy metrics using pre-recorded datasets, often overlooking critical factors such as latency and power consumption. This underscores the need for benchmarks to evaluate real-time performance under noisy, dynamic conditions. To address this need, we developed a system that uses spiking neural networks (SNNs) to control a robotic manipulator in an air-hockey game. In this setup, the automated opponent uses SNNs to process data from an event-based camera, enabling it to track the puck’s movements and respond to the actions of a human player. Our study demonstrates the potential of SNNs to accomplish fast real-time tasks while running on massively parallel hardware. We believe our air-hockey platform provides a versatile testbed for evaluating neuromorphic systems and invites further exploration of advanced algorithms, such as those incorporating trajectory prediction or adaptive learning, which could significantly enhance real-time decision-making and control.https://doi.org/10.1088/2634-4386/addc15event-based visionSpiNNakerLow latencyreal-time |
| spellingShingle | Juan P Romero B Dimitrios Korakovounis Jens E Pedersen Jorg Conradt Low-latency neuromorphic air hockey player Neuromorphic Computing and Engineering event-based vision SpiNNaker Low latency real-time |
| title | Low-latency neuromorphic air hockey player |
| title_full | Low-latency neuromorphic air hockey player |
| title_fullStr | Low-latency neuromorphic air hockey player |
| title_full_unstemmed | Low-latency neuromorphic air hockey player |
| title_short | Low-latency neuromorphic air hockey player |
| title_sort | low latency neuromorphic air hockey player |
| topic | event-based vision SpiNNaker Low latency real-time |
| url | https://doi.org/10.1088/2634-4386/addc15 |
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