将触觉传感器与神经形态芯片直接集成，实现低功耗纹理识别。
GelNeuro: A Sensing-Computing Integrated Neuromorphic Tactile System for Texture Recognition
- 用事件相机捕捉触觉形变，直接在芯片上运行脉冲神经网络分类。
- 80毫秒内达到96.3%准确率，功耗仅19.6毫瓦。
- 适合边缘部署的实时触觉感知，尤其适用于机器人和可穿戴设备。
神经形态视觉-触觉感知为低延迟、低功耗机器人感知提供了新范式。然而，现有系统仍依赖主机计算机进行事件读取、预处理或传输。本文提出GelNeuro，一种完全集成的传感-计算视觉-触觉系统，将基于GelSight Mini的光学触觉前端与Speck2f神经形态系统级芯片（SoC）直接连接。接触引起的标记运动以动态视觉传感器（DVS）事件形式捕获，并通过片上网络传输至脉冲卷积神经网络（SCNN）分类器。为缓解8位部署中的精度下降，引入硬件感知的权重钳制策略。在15类自然纹理识别任务上，物理芯片上的软硬件协同测试在80毫秒推理窗口内实现96.3%准确率。系统板级功耗仅19.6毫瓦，低于传统CPU/GPU基线三个数量级以上。GelNeuro还表现出对未见接触深度的鲁棒泛化能力，验证了边缘神经形态硬件上直接传感器到芯片触觉识别的可行性。
原文摘要 · Abstract (English)
Neuromorphic visuo-tactile sensing offers a promising paradigm for low-latency and low-power robotic perception. However, existing systems still rely heavily on a host computer for event readout, preprocessing, or relaying prior to chip inference. This paper presents GelNeuro, a fully integrated sensing-computing visuo-tactile system that directly pairs a GelSight Mini-based optical tactile front end with the Speck2f neuromorphic system-on-chip (SoC). Contact-induced marker motions are captured as dynamic vision sensor (DVS) events and routed through the on-chip network to a spiking convolutional neural network (SCNN) classifier. To mitigate accuracy degradation during 8-bit deployment, a hardware-aware weight clamping strategy is introduced. Evaluated on a 15-class natural texture recognition task, hardware-in-the-loop testing on the physical chip achieves a 96.3% accuracy within an 80 ms inference window. Notably, the system consumes only 19.6 mW of board-level active power-over three orders of magnitude lower than conventional CPU/GPU baselines on the same benchmark. GelNeuro also exhibits robust generalization across unseen contact depths, demonstrating the viability of direct sensor-to-chip tactile recognition on edge neuromorphic hardware.
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