An Event-Driven E-Skin System with Dynamic Binary Scanning and real time SNN Classification
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866910049121599488 |
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| author | Li, Gaishan Fu, Zhengnan Tripathi, Anubhab Yang, Junyi Basu, Arindam |
| author_facet | Li, Gaishan Fu, Zhengnan Tripathi, Anubhab Yang, Junyi Basu, Arindam |
| contents | This paper presents a novel hardware system for high-speed, event-sparse sampling-based electronic skin (e-skin)that integrates sensing and neuromorphic computing. The system is built around a 16x16 piezoresistive tactile array with front end and introduces a event-based binary scan search strategy to classify the digits. This event-driven strategy achieves a 12.8x reduction in scan counts, a 38.2x data compression rate and a 28.4x equivalent dynamic range, a 99% data sparsity, drastically reducing the data acquisition overhead. The resulting sparse data stream is processed by a multi-layer convolutional spiking neural network (Conv-SNN) implemented on an FPGA, which requires only 65% of the computation and 15.6% of the weight storage relative to a CNN. Despite these significant efficiency gains, the system maintains a high classification accuracy of 92.11% for real-time handwritten digit recognition. Furthermore, a real neuromorphic tactile dataset using Address Event Representation (AER) is constructed. This work demonstrates a fully integrated, event-driven pipeline from analog sensing to neuromorphic classification, offering an efficient solution for robotic perception and human-computer interaction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_10537 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | An Event-Driven E-Skin System with Dynamic Binary Scanning and real time SNN Classification Li, Gaishan Fu, Zhengnan Tripathi, Anubhab Yang, Junyi Basu, Arindam Neural and Evolutionary Computing This paper presents a novel hardware system for high-speed, event-sparse sampling-based electronic skin (e-skin)that integrates sensing and neuromorphic computing. The system is built around a 16x16 piezoresistive tactile array with front end and introduces a event-based binary scan search strategy to classify the digits. This event-driven strategy achieves a 12.8x reduction in scan counts, a 38.2x data compression rate and a 28.4x equivalent dynamic range, a 99% data sparsity, drastically reducing the data acquisition overhead. The resulting sparse data stream is processed by a multi-layer convolutional spiking neural network (Conv-SNN) implemented on an FPGA, which requires only 65% of the computation and 15.6% of the weight storage relative to a CNN. Despite these significant efficiency gains, the system maintains a high classification accuracy of 92.11% for real-time handwritten digit recognition. Furthermore, a real neuromorphic tactile dataset using Address Event Representation (AER) is constructed. This work demonstrates a fully integrated, event-driven pipeline from analog sensing to neuromorphic classification, offering an efficient solution for robotic perception and human-computer interaction. |
| title | An Event-Driven E-Skin System with Dynamic Binary Scanning and real time SNN Classification |
| topic | Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2603.10537 |