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Related Concept Videos

Parallel Processing01:20

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The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
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Sepia, Tarsier, and Chameleon: A Modular C++ Framework for Event-Based Computer Vision.

Alexandre Marcireau1, Sio-Hoi Ieng1, Ryad Benosman1,2,3

  • 1INSERM UMRI S 968, Sorbonne Universites, UPMC Univ Paris 06, UMR S 968, CNRS, UMR 7210, Institut de la Vision, Paris, France.

Frontiers in Neuroscience
|January 24, 2020
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Summary
This summary is machine-generated.

This new C++ framework enables faster event-driven algorithms using static polymorphism. Its modular design and observer pattern simplify development and support neuromorphic hardware translation.

Keywords:
asynchronous computationdevelopment frameworkevent-based processingevent-based sensingsilicon retinas

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Area of Science:

  • Computer Science
  • Computational Neuroscience
  • Algorithm Development

Background:

  • Event-driven algorithms are crucial for real-time data processing.
  • Existing frameworks may lack efficiency and flexibility for complex event-driven tasks.
  • Neuromorphic hardware requires specialized algorithm implementations.

Purpose of the Study:

  • Introduce a novel, open-source C++ framework for event-driven algorithms.
  • Enhance algorithm performance through static polymorphism and modular design.
  • Facilitate the translation of algorithms to neuromorphic hardware.

Main Methods:

  • Developed a modular C++ framework with three components: sepia (file IO), tarsier (algorithms), and chameleon (display).
  • Utilized static polymorphism for compile-time pipeline assembly to optimize performance.
  • Implemented the observer pattern to align with event-driven processing paradigms.

Main Results:

  • Algorithms implemented with the 'tarsier' component demonstrated superior speed and lower latency compared to other frameworks.
  • Benchmarks confirmed the efficiency gains from static polymorphism.
  • The framework successfully integrated drivers for various event-based cameras (DVS, DAVIS, ATIS).

Conclusions:

  • The new framework provides a high-performance, flexible solution for implementing event-driven algorithms.
  • Its design facilitates easier development and potential adaptation for neuromorphic computing.
  • The framework's efficiency and modularity make it a valuable tool for researchers and developers.