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

TL;DR

This paper introduces an innovative event-driven e-skin system that combines high-speed sensing with neuromorphic classification, significantly reducing data overhead while maintaining high accuracy for real-time digit recognition.

Contribution

The work presents a novel hardware system integrating event-based sensing, binary scanning, and neuromorphic computing on FPGA, achieving high efficiency and accuracy in tactile digit classification.

Findings

12.8x reduction in scan counts

92.11% classification accuracy

38.2x data compression rate

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.