一个高性能的像素级完全管道硬件加速器用于神经网络.
概括
这项研究介绍了一种用于卷积神经网络 (CNN) 的全管道硬件加速器. 这种像素对像素的设计显著提高了计算机视觉任务的处理速度和吞吐量,克服了现有的单个或多个计算引擎架构的局限性.
科学领域:
- 计算机工程 计算机工程
- 人工智能的人工智能
- 硬件加速器 硬件加速器
背景情况:
- 基于单个或多个计算引擎 (CE) 架构的当前卷积神经网络 (CNN) 硬件加速器在资源利用和数据吞吐量方面面临限制.
- 现有的加速器与大型功能地图作斗争,通常达到低至10/秒的速度,这对于自动驾驶和雷达检测等苛刻的应用来说是不够的.
研究的目的:
- 为了解决现有的CNN硬件加速器的性能瓶.
- 提出一个新的,完全管道化的硬件加速器设计,优化用于像素级处理.
- 为了提高CNN的资源利用率和数据吞吐量,从而实现高速AI应用.
主要方法:
- 引入了像素对像素的处理策略,最大限度地降低了传统基于层的计算的重要性.
- 通过将每个神经网络层扩展到硬件中,开发出一个完全管道化的系统,消除了层间缓冲器.
- 通过优化每个输出功能地图 (Ofmap) 像素的生成,最大化了整个网络的完整连接性.
主要成果:
- 实现了4205.50/s的处理速度和4787.15GOP/s的吞吐量在211MHz的MobileNetV1加速器.
- 显示出每个图像的输出延迟为0.60毫秒,显著减少处理时间.
- 证实了加速器在各种CNN模型中的普遍性,包括MobileNetV1,MobileNetV2和FashionNet.
结论:
- 拟议的全管道,基于像素的硬件加速器设计比现有架构提供了大幅度的性能改进.
- 这种方法有效地克服了资源利用和数据吞吐量方面的局限性,使CNN能够用于高速应用.
- 它的通用设计适用于各种计算机视觉任务,为实时场景中先进的AI部署铺平了道路.
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