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.

翻译：本文提出了一种新颖的硬件系统，用于实现基于高速、事件稀疏采样的电子皮肤（e-skin），该系统集成了传感与神经形态计算功能。该系统围绕一个16x16的压阻式触觉阵列及其前端电路构建，并引入了一种基于事件的二进制扫描搜索策略以进行数字分类。该事件驱动策略实现了扫描次数降低12.8倍、数据压缩率达到38.2倍、等效动态范围提升28.4倍以及99%的数据稀疏度，从而大幅降低了数据采集开销。产生的稀疏数据流由一个在FPGA上实现的多层卷积脉冲神经网络（Conv-SNN）进行处理，相对于CNN，其仅需65%的计算量和15.6%的权重存储量。尽管取得了这些显著的效率提升，该系统在实时手写数字识别任务中仍保持了92.11%的高分类准确率。此外，本研究还利用地址事件表示（AER）构建了一个真实的神经形态触觉数据集。这项工作展示了一个从模拟感知到神经形态分类的、完全集成的事件驱动处理流程，为机器人感知和人机交互提供了一种高效的解决方案。