Szczegóły publikacji
Opis bibliograficzny
End-to-end keyword spotting on FPGA using graph neural networks with a neuromorphic auditory sensor / Wiktor Matykiewicz, Piotr WZOREK, Kamil JEZIOREK, Tomás Muñoz, Antonio Rios-Navarro, Angel Jiménez-Fernández, Tomasz KRYJAK // W: Applied Reconfigurable Computing : architectures, tools, and applications : 22nd international symposium, ARC 2026 : Cagliari, Sardinia, Italy, April 8–10, 2026 : proceedings / eds. Gianluca Leone, [et al.]. — Cham : Springer, cop. 2026. — ( Lecture Notes in Computer Science ; ISSN 0302-9743 ; LNCS 16514 ). — ISBN: 978-3-032-29364-0; e-ISBN: 978-3-032-29365-7. — S. 119–136. — Bibliogr., Abstr. — Publikacja dostępna online od: 2026-06-30. — P. Wzorek, K. Jeziorek, T. Kryjak – dod. afiliacja: Embedded Vision Systems Group, Computer Vision Laboratory, Kraków, Poland
Autorzy (7)
- AGHMatykiewicz Wiktor
- AGHWzorek Piotr
- AGHJeziorek Kamil
- Muñoz Tomás
- Rios-Navarro Antonio
- Jiménez-Fernández Angel
- AGHKryjak Tomasz
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 168992 |
|---|---|
| Data dodania do BaDAP | 2026-08-31 |
| DOI | 10.1007/978-3-032-29365-7_8 |
| Rok publikacji | 2026 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Springer |
| Czasopismo/seria | Lecture Notes in Computer Science |
Abstract
With the rapid growth of mobile robotics and embedded intelligence, there is an increasing demand for efficient on-device data processing on edge platforms. A promising research direction is the use of neuromorphic sensors inspired by human sensory systems, which generate sparse, event-based data encoding changes in the environment. In this work, we present the first end-to-end FPGA implementation of a keyword spotting system that integrates a Neuromorphic Auditory Sensor (NAS) and a graph neural network (GNN) on a single FPGA device, enabling real-time processing of raw audio data. The proposed architecture eliminates conventional signal preprocessing and operates directly on event-based audio streams. Leveraging a compute-near-memory network architecture, the system achieves efficient inference with low latency and low power consumption. Experimental results demonstrate an accuracy of 87.43% after quantization on the Google Speech Commands v2 dataset processed through the neuromorphic sensor, with end-to-end latency below 35 μs and average power consumption of 1.12 W. The processed datasets, software models, and hardware modules are available at https://github.com/vision-agh/NAS-GNN-KWS.
