基于ACTEL IGLOO低功耗FPGA的高性能DT-CNN摄像器件设计

S. Consul-Pacareu, J. Albó-Canals, Xavier Vilasís-Cardona, J. Riera-Babures
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引用次数: 7

摘要

在本文中,我们在不使用昂贵元件的情况下,对高性能图像处理和低功耗之间的平衡进行了全面的研究。我们的建议是在低功耗Actel IGLOO纳米现场可编程门阵列(FPGA)上实现离散时间细胞神经网络(DT-CNN)。这是一个决定性的一步,从以前的工作,以获得智能相机设备的机器人。近年来,随着机器人在日常生活的不同领域的突破,机器人导航的应用迅速增加,大多数机器人设备都需要传感器进行导航。我们提出的低成本解决方案避免了高度复杂的架构,昂贵的智能传感器和低性能的导航系统。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
High performance DT-CNN camera device design on ACTEL IGLOO low power FPGA
In this paper we present a complete study on the balance between high performance image processing and low power consumption without using expensive components. Our proposal consists in implementing a Discrete Time Cellular Neural Network (DT-CNN) on a low power Actel IGLOO nano Field Programmable Gate Array (FPGA). This is a definitive step further from previous work to obtain an intelligent camera device for robots. Applications in Robot Guidance have rapidly increased in the last years as robots break in different fields of everyday live, which most of this robotic devices need sensors for navigation. Our proposed low cost solution avoids highly complex architectures, expensive smart sensors and low performance navigation systems.
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