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 us and average power consumption of 1.12 W. The processed datasets, software models, and hardware modules are available at this https URL.
| Comments: | Accepted for the ARC 2026 conference |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.09570 [cs.LG] |
| (or arXiv:2605.09570v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.09570 arXiv-issued DOI via DataCite (pending registration) |
From: Tomasz Kryjak [view email]
[v1]
Sun, 10 May 2026 14:31:01 UTC (13,777 KB)
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