Accelerating Sensor Fusion in Neuromorphic Computing: A Case Study on Loihi-2
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866914928269459456 |
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| author | Isik, Murat Tiwari, Karn Eryilmaz, Muhammed Burak Dikmen, I. Can |
| author_facet | Isik, Murat Tiwari, Karn Eryilmaz, Muhammed Burak Dikmen, I. Can |
| contents | In our study, we utilized Intel's Loihi-2 neuromorphic chip to enhance sensor fusion in fields like robotics and autonomous systems, focusing on datasets such as AIODrive, Oxford Radar RobotCar, D-Behavior (D-Set), nuScenes by Motional, and Comma2k19. Our research demonstrated that Loihi-2, using spiking neural networks, significantly outperformed traditional computing methods in speed and energy efficiency. Compared to conventional CPUs and GPUs, Loihi-2 showed remarkable energy efficiency, being over 100 times more efficient than a CPU and nearly 30 times more than a GPU. Additionally, our Loihi-2 implementation achieved faster processing speeds on various datasets, marking a substantial advancement over existing state-of-the-art implementations. This paper also discusses the specific challenges encountered during the implementation and optimization processes, providing insights into the architectural innovations of Loihi-2 that contribute to its superior performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_16096 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Accelerating Sensor Fusion in Neuromorphic Computing: A Case Study on Loihi-2 Isik, Murat Tiwari, Karn Eryilmaz, Muhammed Burak Dikmen, I. Can Hardware Architecture In our study, we utilized Intel's Loihi-2 neuromorphic chip to enhance sensor fusion in fields like robotics and autonomous systems, focusing on datasets such as AIODrive, Oxford Radar RobotCar, D-Behavior (D-Set), nuScenes by Motional, and Comma2k19. Our research demonstrated that Loihi-2, using spiking neural networks, significantly outperformed traditional computing methods in speed and energy efficiency. Compared to conventional CPUs and GPUs, Loihi-2 showed remarkable energy efficiency, being over 100 times more efficient than a CPU and nearly 30 times more than a GPU. Additionally, our Loihi-2 implementation achieved faster processing speeds on various datasets, marking a substantial advancement over existing state-of-the-art implementations. This paper also discusses the specific challenges encountered during the implementation and optimization processes, providing insights into the architectural innovations of Loihi-2 that contribute to its superior performance. |
| title | Accelerating Sensor Fusion in Neuromorphic Computing: A Case Study on Loihi-2 |
| topic | Hardware Architecture |
| url | https://arxiv.org/abs/2408.16096 |