Comparing three online evolvable hardware implementations of a classification system

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.

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