An Event-Driven E-Skin System with Dynamic Binary Scanning and real time SNN Classification

• URL: http://arxiv.org/abs/2603.10537v1
• Date: Wed, 11 Mar 2026 08:42:05 GMT
• Title: An Event-Driven E-Skin System with Dynamic Binary Scanning and real time SNN Classification
• Abstract summary: This paper presents a novel hardware system for high-speed, event-sparse sampling-based electronic skin (e-skin)<n>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.<n>Despite significant efficiency gains, the system maintains a high classification accuracy of 92.11% for real-time handwritten digit recognition.
• Score: 4.939047188463064
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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