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Communication Dans Un Congrès Année : 2023

Performance comparison of DVS data spatial downscaling methods using Spiking Neural Networks

Résumé

Dynamic Vision Sensors (DVS) are an unconventional type of camera that produces sparse and asynchronous event data, which has recently led to a strong increase in its use for computer vision tasks namely in robotics. Embedded systems face limitations in terms of energy resources, memory, computational power, and communication bandwidth. Hence, this application calls for a way to reduce the amount of data to be processed while keeping the relevant information for the task at hand. We thus believe that a formal definition of event data reduction methods will provide a step further towards sparse data processing. The contributions of this paper are twofold: we introduce two complementary neuromorphic methods based on Spiking Neural Networks for DVS data spatial reduction, which is to best of our knowledge the first proposal of neuromorphic event data reduction; then we study for each method the trade-off between the amount of information kept after reduction, the performance of gesture classification after reduction and their capacity to handle events in real time. We demonstrate here that the proposed SNNbased methods outperform existing methods in a classification task for most dividing factors and are significantly better at handling data in real time, and make therefore the optimal choice for fully-integrated energy-efficient event data reduction running dynamically on a neuromorphic platform.
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Dates et versions

hal-04090844 , version 1 (06-05-2023)

Identifiants

  • HAL Id : hal-04090844 , version 1

Citer

Amélie Gruel, Jean Martinet, Bernabé Linares-Barranco, Teresa Serrano-Gotarredona. Performance comparison of DVS data spatial downscaling methods using Spiking Neural Networks. IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Jan 2023, Waikoloa, Hawaii, United States. pp.6494-6502. ⟨hal-04090844⟩
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