Marcireau Alexandre, Ieng Sio-Hoi, Benosman Ryad
INSERM UMRI S 968, Sorbonne Universites, UPMC Univ Paris 06, UMR S 968, CNRS, UMR 7210, Institut de la Vision, Paris, France.
University of Pittsburgh Medical Center, Pittsburgh, PA, United States.
Front Neurosci. 2020 Jan 8;13:1338. doi: 10.3389/fnins.2019.01338. eCollection 2019.
This paper introduces an new open-source, header-only and modular C++ framework to facilitate the implementation of event-driven algorithms. The framework relies on three independent components: (file IO), (algorithms), and (display). Our benchmarks show that algorithms implemented with are faster and have a lower latency than identical implementations in other state-of-the-art frameworks, thanks to static polymorphism (compile-time pipeline assembly). The used throughout the framework encourages implementations that better reflect the event-driven nature of the algorithms and the way they process events, easing future translation to neuromorphic hardware. The framework integrates drivers to communicate with the , the , the , and the .
本文介绍了一个新的开源、仅含头文件且模块化的C++框架，以促进事件驱动算法的实现。该框架依赖于三个独立组件：（文件输入/输出）、（算法）和（显示）。我们的基准测试表明，由于静态多态性（编译时流水线组装），使用该框架实现的算法比其他最先进框架中的相同实现更快且延迟更低。整个框架中使用的（此处原文未明确该词含义）鼓励采用能更好地反映算法的事件驱动性质及其处理事件方式的实现方式，便于未来向神经形态硬件进行转换。该框架集成了驱动程序，以便与（此处原文未明确相关设备名称）、（此处原文未明确相关设备名称）、（此处原文未明确相关设备名称）和（此处原文未明确相关设备名称）进行通信。
