Radar-Based Gesture Recognition with Spiking Neural Networks
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Beitragende
Abstract
Spiking neural networks (SNN) are a promising approach for low-power edge AI (artificial intelligence), especially when run on dedicated neuromorphic hardware. In this work we set up a SNN in TensorFlow, directly train it in a supervised manner with backpropagation through time (BPTT) and surrogate gradients, and compare it with traditional neural networks, based on convolutional neural networks (CNN) and long-short term memory (LSTM) cells, for radar-based hand-gesture recognition. We demonstrate that a small SNN with only 30 hidden leaky integrate-and-fire (LIF) neurons and threshold encoding can achieve an accuracy of 98.1%. With the more complex adaptive LIF neuron, the activity can be reduced by up to 37.9% without significant loss in accuracy. A comparison to traditional LSTM-networks shows the superiority of the SNN in terms of accuracy and computation cost, indicating that they are a considerable alternative to LSTM-based approaches.
Details
Originalsprache | Englisch |
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Titel | 2022 7th International Conference on Frontiers of Signal Processing, ICFSP 2022 |
Herausgeber (Verlag) | Institute of Electrical and Electronics Engineers Inc. |
Seiten | 40-44 |
Seitenumfang | 5 |
ISBN (elektronisch) | 978-1-6654-8158-8 |
Publikationsstatus | Veröffentlicht - 2022 |
Peer-Review-Status | Ja |
Konferenz
Titel | 7th International Conference on Frontiers of Signal Processing, ICFSP 2022 |
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Dauer | 7 - 9 September 2022 |
Stadt | Paris |
Land | Frankreich |
Schlagworte
ASJC Scopus Sachgebiete
Schlagwörter
- gesture recognition, radar, spiking neural networks, surrogate gradient