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ACM Interact. Mob. Wearable Ubiquitous Technol."],"published-print":{"date-parts":[[2025,12,2]]},"abstract":"<jats:p>\n                    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\n                    <jats:sc>pike<\/jats:sc>\n                    T\n                    <jats:sc>ouch<\/jats:sc>\n                    , a software framework designed to reduce the computational overhead of SNNs for tactile perception on neuromorphic hardware. S\n                    <jats:sc>pike<\/jats:sc>\n                    T\n                    <jats:sc>ouch<\/jats:sc>\n                    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\n                    <jats:sc>pike<\/jats:sc>\n                    T\n                    <jats:sc>ouch<\/jats:sc>\n                    on the Tianjic neuromorphic chip using representative tactile perception workloads. Our results show that S\n                    <jats:sc>pike<\/jats:sc>\n                    T\n                    <jats:sc>ouch<\/jats:sc>\n                    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.\n                  <\/jats:p>","DOI":"10.1145\/3770658","type":"journal-article","created":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T19:42:32Z","timestamp":1764704552000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["S\n                    <scp>pike<\/scp>\n                    T\n                    <scp>ouch<\/scp>\n                    : Optimizing Spike Neural Networks for Tactile Perception"],"prefix":"10.1145","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-4305-0977","authenticated-orcid":false,"given":"Xuerong","family":"Zhao","sequence":"first","affiliation":[{"name":"School of Information Science and Technology, Northwest University, Xi'an, Shan Xi, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6271-0388","authenticated-orcid":false,"given":"Xuan","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information science and Technology, Northwest University, Xi'an, Shan Xi, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-8393-1147","authenticated-orcid":false,"given":"Jian","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Northwest University, Xi'an, Shaanxi, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7322-2320","authenticated-orcid":false,"given":"Chao","family":"Feng","sequence":"additional","affiliation":[{"name":"Northwest University, xi an, shaanxi, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5816-6922","authenticated-orcid":false,"given":"Dingyi","family":"Fang","sequence":"additional","affiliation":[{"name":"Northwest University, Xi'an, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1180-6806","authenticated-orcid":false,"given":"Xiaojiang","family":"Chen","sequence":"additional","affiliation":[{"name":"Northwest University, Xi'an, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6157-0662","authenticated-orcid":false,"given":"Zheng","family":"Wang","sequence":"additional","affiliation":[{"name":"University of Leeds, Leeds, United Kingdom"}]}],"member":"320","published-online":{"date-parts":[[2025,12,2]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"[n. d.]. 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