Guardado en:
| Autores principales: | , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2408.05846 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866915306539057152 |
|---|---|
| author | Liu, Jialin Liao, Diansheng |
| author_facet | Liu, Jialin Liao, Diansheng |
| contents | Extremely increased unstructured data brought by the large-scale intelligent sensing devices application have big challenges not only in data storing and processing but also power consumption surging. Therefore, to improve energy efficiency and processing speed, a new generation system structure and construction strategy is necessary. Most biological nervous systems, especially the tactile system, have a good flexibility and data processing performance with low power usage. Inspired from this mechanism, to optimize the intelligent system, we report a universal fully flexible neuromorphic perception system with a strong compatibility and multi-threshold signal processing strategy by mimicking tactile nervous system. Peak signal accumulated from spike encoded sensor signal in front-end processing unit can be used for recognition task directly since the bionic synaptic plasticity. Compared with conventional systems, power consumption of our system significantly decreases about 1 order of magnitude in a same recognition task. What is more, the design of voltage-based matching circuit and multithreshold processing circuit provide an excellent compatibility and multi-signal processing capability in our system. In feasibility verification, our system can output trend of different input signals (continuous signal and frequency signal etc.) accurately and have a high recognition accuracy of 90% in the symbol pattern and 90% in Morse code. These properties of our neuromorphic system show a great application potential in intelligent devices and bionic robots. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_05846 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Universal Flexible Neuromorphic Tactile System with Multithreshold Strategy Liu, Jialin Liao, Diansheng Neural and Evolutionary Computing Robotics Extremely increased unstructured data brought by the large-scale intelligent sensing devices application have big challenges not only in data storing and processing but also power consumption surging. Therefore, to improve energy efficiency and processing speed, a new generation system structure and construction strategy is necessary. Most biological nervous systems, especially the tactile system, have a good flexibility and data processing performance with low power usage. Inspired from this mechanism, to optimize the intelligent system, we report a universal fully flexible neuromorphic perception system with a strong compatibility and multi-threshold signal processing strategy by mimicking tactile nervous system. Peak signal accumulated from spike encoded sensor signal in front-end processing unit can be used for recognition task directly since the bionic synaptic plasticity. Compared with conventional systems, power consumption of our system significantly decreases about 1 order of magnitude in a same recognition task. What is more, the design of voltage-based matching circuit and multithreshold processing circuit provide an excellent compatibility and multi-signal processing capability in our system. In feasibility verification, our system can output trend of different input signals (continuous signal and frequency signal etc.) accurately and have a high recognition accuracy of 90% in the symbol pattern and 90% in Morse code. These properties of our neuromorphic system show a great application potential in intelligent devices and bionic robots. |
| title | A Universal Flexible Neuromorphic Tactile System with Multithreshold Strategy |
| topic | Neural and Evolutionary Computing Robotics |
| url | https://arxiv.org/abs/2408.05846 |