S pike T ouch : Optimizing Spike Neural Networks for Tactile Perception

Tactile perception enables systems to sense and interpret physical properties such as shape, material, pressure, and texture. It is a key capability for emerging applications like robotic surgery and assistive robotics. Existing solutions to tactile perception typically rely on computation-intensive deep neural networks, which require high-performance computing resources unavailable on embedded and battery-powered devices. Spiking neural networks (SNNs) offer a promising, energy-efficient alternative, but their practical adoption remains limited due to the lack of efficient and deployable neuromorphic solutions. We present S pike T ouch , a software framework designed to reduce the computational overhead of SNNs for tactile perception on neuromorphic hardware. S pike T ouch offers three key optimizations tailed to SNN-based tactile perception: (1) a spike encoding scheme that balances precision and computational cost; (2) a systematic method for extracting multidimensional tactile features; and (3) a training strategy that minimizes quantization errors to improve performance and reduce memory usage. We evaluate S pike T ouch on the Tianjic neuromorphic chip using representative tactile perception workloads. Our results show that S pike T ouch achieves high recognition accuracy for 30 objects and their material stiffness, with an average accuracy of 92.92% and 92.35%, respectively. It is also highly energy and computationally efficient, operating at just 1 Watt of power - significantly lower than the hundreds or thousands of Watts typically required by a GPU - and delivering a response in under 20 ms - well below the average human reflex time of over 100 ms.