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GelNeuro: A Sensing-Computing Integrated Neuromorphic Tactile System for Texture Recognition

arxiv.org · 6,128 words · saved by 1 readers

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 thre

Xinpan Meng Zhenghua Ma Houcheng Li Long Cheng ††thanks: This work was supported in part by the Brain Science and Brain-like Intelligence Technology - National Science and Technology Major Project under Grant 2025ZD0215600, in part by the National Natural Science Foundation of China under Grant U25A20475, in part by the Beijing Municipal Natural Science Foundation under Grants F2024201068, L243014 and L232140, in part by Young Scientists Fund of The State Key Laboratory of Multimodal Artificial Intelligence Systems under Grant ES2P100114, and in part by the Fundamental Research Funds for…

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