Real-Time Adaptive Physical Sensor Processing with SNN Hardware
| dc.contributor.author | Madrenas, Jordi | |
| dc.contributor.author | Vallejo-Mancero, Bernardo | |
| dc.contributor.author | Oltra-Oltra, Josep | |
| dc.contributor.author | Zapata, Mireya | |
| dc.contributor.author | Cosp-Vilella, Jordi | |
| dc.contributor.author | Calatayud, Robert | |
| dc.contributor.author | Moriya, Satoshi | |
| dc.contributor.author | Sato, Shigeo | |
| dc.date.accessioned | 2023-12-20T21:46:40Z | |
| dc.date.available | 2023-12-20T21:46:40Z | |
| dc.date.issued | 2023 | |
| dc.description.abstract | Spiking Neural Networks (SNNs) offer bioinspired computation based on local adaptation and plasticity as well as close biological compatibility. In this work, after reviewing the Hardware Emulator of Evolving Neural Systems (HEENS) architecture and its Computer-Aided Engineering (CAE) design flow, a spiking implementation of an adaptive physical sensor input scheme based on time-rate Band-Pass Filter (BPF) is proposed for real-time execution of large dynamic range sensory edge processing nodes. Simulation and experimental results of the SNN operating in real-time with an adaptive-range accelerometer input example are shown. This work opens the path to compute with SNNs multiple physical sensor information for perception applications. | es |
| dc.identifier.uri | https://link.springer.com/chapter/10.1007/978-3-031-44192-9_34 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.14809/6129 | |
| dc.language.iso | eng | es |
| dc.publisher | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Volume 14258 LNCS, Pages 423 - 434 | es |
| dc.rights | openAccess | es |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | es |
| dc.title | Real-Time Adaptive Physical Sensor Processing with SNN Hardware | es |
| dc.type | article | es |