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Comparing three online evolvable hardware implementations of a classification system
Genetic Programming and Evolvable Machines ( IF 2.6 ) Pub Date : 2017-10-13 , DOI: 10.1007/s10710-017-9312-1
Oscar Garnica , Kyrre Glette , Jim Torresen

In this paper, we present three implementations of an online evolvable hardware classifier of sonar signals on a 28 nm process technology FPGA, and compare their features using the most relevant metrics in the design of hardware: area, timing, power consumption, energy consumption, and performance. The three implementations are: one full-hardware implementation in which all the modules of the evolvable hardware system, the evaluation module and the Evolutionary Algorithm have been implemented on the ZedBoard™ Zynq® Evaluation Kit (XC7-Z020 ELQ484-1); and two hardware/software implementations in which the Evolutionary Algorithm has been implemented in software and run on two different processors: Zynq® XC7-Z020 and MicroBlaze™. Additionally, each processor-based implementation has been tested at several processor speeds. The results prove that the full-hardware implementation always performs better than the hardware/software implementations by a considerable margin: up to $$\times \,7.74$$×7.74 faster than MicroBlaze, between $$\times \,1.39$$×1.39 and $$\times \,2.11$$×2.11 faster that Zynq, and $$\times \,0.198$$×0.198 lower power consumption. However, the hardware/software implementations have the advantage of being more flexible for testing different options during the design phase. These figures can be used as a guideline to determine the best use for each kind of implementation.

中文翻译:

比较分类系统的三个在线可进化硬件实现

在本文中,我们在 28 nm 工艺技术 FPGA 上介绍了声纳信号的在线可演化硬件分类器的三种实现,并使用硬件设计中最相关的指标来比较它们的特性:面积、时序、功耗、能耗、和性能。这三种实现是: 一种全硬件实现,其中可进化硬件系统、评估模块和进化算法的所有模块都已在 ZedBoard™ Zynq® 评估套件 (XC7-Z020 ELQ484-1) 上实现;以及两种硬件/软件实现,其中进化算法已在软件中实现并在两种不同的处理器上运行:Zynq® XC7-Z020 和 MicroBlaze™。此外,每个基于处理器的实现都已在多种处理器速度下进行了测试。结果证明,全硬件实现的性能总是比硬件/软件实现好很多:比 MicroBlaze 快 $$\times \,7.74$$×7.74,介于 $$\times \,1.39$$ ×1.39 和 $$\times \,2.11$$×2.11 比 Zynq 快,$$\times \,0.198$$×0.198 更低的功耗。然而,硬件/软件实现的优势在于在设计阶段更灵活地测试不同的选项。这些数字可用作确定每种实现的最佳用途的指南。198 更低的功耗。然而,硬件/软件实现的优势在于在设计阶段更灵活地测试不同的选项。这些数字可用作确定每种实现的最佳用途的指南。198 更低的功耗。然而,硬件/软件实现的优势在于在设计阶段更灵活地测试不同的选项。这些数字可用作确定每种实现的最佳用途的指南。
更新日期:2017-10-13
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