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

2026-03-11

AI Summary

This work proposes an event-driven electronic skin system to address the high data redundancy, excessive power consumption, and limited real-time processing capability of conventional e-skins under high sampling rates. By integrating a dynamic binary scanning strategy with a multi-layer convolutional spiking neural network (Conv-SNN), the system implements an efficient FPGA-based pipeline that spans from tactile sensing to real-time classification. The study further introduces a novel neuromorphic tactile dataset based on the Address-Event Representation (AER) protocol. Experimental results demonstrate substantial reductions in system overhead: scanning operations are reduced by 12.8×, data volume is compressed by 38.2×, and data sparsity reaches 99%, while achieving a classification accuracy of 92.11%. Compared to traditional CNNs, the proposed approach reduces computational load to 65% and weight storage to 15.6%.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Hardware-aware ML
• Humans and AI: Brain-Sensing and Analysis

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Security and Privacy: Data transparency and provenance

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.

Problem

• electronic skin
• event-driven sensing
• spiking neural network
• data sparsity
• real-time classification

Innovation

• event-driven
• spiking neural network
• electronic skin
• dynamic binary scanning
• neuromorphic computing