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Neuromorphic computation using event-based sensors : from algorithms to hardware implementations

Abstract : This thesis is about the implementation of neuromorphic algorithms, using, as a first step, data from a silicon retina, mimicking the human eye’s behavior, and then evolve towards all kind of event-based signals. These eventbased signals are coming from a paradigm shift in the data representation, thus allowing a high dynamic range, a precise temporal resolution and a sensor-level data compression. Especially, we will study the development of a high frequency monocular depth map generator, a real-time spike sorting algorithm for intelligent brain-machine interfaces, and an unsupervised learning algorithm for pattern recognition. Some of these algorithms (Optical flow detection, depth map construction from stereovision) will be in the meantime developed on available neuromorphic platforms (SpiNNaker, TrueNorth), thus allowing a fully neuromorphic pipeline, from sensing to computing, with a low power budget.
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Submitted on : Friday, December 13, 2019 - 4:52:17 PM
Last modification on : Monday, January 13, 2020 - 1:18:54 AM


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  • HAL Id : tel-02410130, version 1


Germain Haessig. Neuromorphic computation using event-based sensors : from algorithms to hardware implementations. Automatic. Sorbonne Université, 2018. English. ⟨NNT : 2018SORUS422⟩. ⟨tel-02410130⟩



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