EventF2S: Asynchronous and Sparse Spiking AER Framework using Neuromorphic-Friendly Algorithm
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866910332580003840 |
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| author | Annamalai, Lakshmi Thakur, Chetan Singh |
| author_facet | Annamalai, Lakshmi Thakur, Chetan Singh |
| contents | Bio-inspired Address Event Representation (AER) sensors have attracted significant popularity owing to their low power consumption, high sparsity, and high temporal resolution. Spiking Neural Network (SNN) has become the inherent choice for AER data processing. However, the integration of the AER-SNN paradigm has not adequately explored asynchronous processing, neuromorphic compatibility, and sparse spiking, which are the key requirements of resource-constrained applications. To address this gap, we introduce a brain-inspired AER-SNN object recognition solution, which includes a data encoder integrated with a First-To-Spike recognition network. Being fascinated by the functionality of neurons in the visual cortex, we designed the solution to be asynchronous and compatible with neuromorphic hardware. Furthermore, we have adapted the principle of denoising and First-To-Spike coding to achieve optimal spike signaling, significantly reducing computation costs. Experimental evaluation has demonstrated that the proposed method incurs significantly less computation cost to achieve state-of-the-art competitive accuracy. Overall, the proposed solution offers an asynchronous and cost-effective AER recognition system that harnesses the full potential of AER sensors. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_10078 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | EventF2S: Asynchronous and Sparse Spiking AER Framework using Neuromorphic-Friendly Algorithm Annamalai, Lakshmi Thakur, Chetan Singh Neural and Evolutionary Computing Machine Learning Signal Processing Bio-inspired Address Event Representation (AER) sensors have attracted significant popularity owing to their low power consumption, high sparsity, and high temporal resolution. Spiking Neural Network (SNN) has become the inherent choice for AER data processing. However, the integration of the AER-SNN paradigm has not adequately explored asynchronous processing, neuromorphic compatibility, and sparse spiking, which are the key requirements of resource-constrained applications. To address this gap, we introduce a brain-inspired AER-SNN object recognition solution, which includes a data encoder integrated with a First-To-Spike recognition network. Being fascinated by the functionality of neurons in the visual cortex, we designed the solution to be asynchronous and compatible with neuromorphic hardware. Furthermore, we have adapted the principle of denoising and First-To-Spike coding to achieve optimal spike signaling, significantly reducing computation costs. Experimental evaluation has demonstrated that the proposed method incurs significantly less computation cost to achieve state-of-the-art competitive accuracy. Overall, the proposed solution offers an asynchronous and cost-effective AER recognition system that harnesses the full potential of AER sensors. |
| title | EventF2S: Asynchronous and Sparse Spiking AER Framework using Neuromorphic-Friendly Algorithm |
| topic | Neural and Evolutionary Computing Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2402.10078 |