In a relatively short time (e.g., seconds), HexArray is able to evolve autonomously to the desired filter.īy exploiting the routing flexibility in the HexArray architecture, the EHW has a simple yet effective mechanism to detect and tolerate faulty cells, which improves system reliability. Through an iterative process using the genetic operators and a fitness function, the EHW system configures and adapts itself to evolve fitter solutions. A computationally intensive application that evolves adaptive filters for image processing was chosen as a case study and used to conduct a set of experiments to prove the developed system robustness. The system was implemented on a SoC that includes a programmable logic (i.e., field-programmable gate array) to realize the HexArray and a processing system to execute the GAGA. These operators improve evolution while not limiting the algorithm from exploring all areas of a solution space. The GAX operator cascades, interleaves, or parallel-recombines genomes at the cell level to generate better genomes. The GAM operator restricts the mutation to the part of the genome that affects the selected output. The GAC selection operator improves parallelism and reduces the redundant evaluations. The operators are genome-aware constrained (GAC) selection, genome-aware mutation (GAM), and genome-aware crossover (GAX). Guided by a fitness function the GAGA utilizes context-aware genetic operators to evolve solutions. The improved evolutionary algorithm is a genome-aware genetic algorithm (GAGA) that accelerates evolution. HexArray was constructed using processing elements with a redesigned architecture, called HexCells, which provide routing flexibility and support for hybrid reconfiguration schemes. The proposed reconfigurable hardware core is a systolic array, which is called HexArray. In this work, a novel evolvable hardware platform is proposed that combines a novel reconfigurable hardware core and a novel evolutionary algorithm. Thus, current implementations suffer from being application oriented and having slow reconfiguration times, low efficiencies, and less routing flexibility. The majority of prior research focuses on improving either the reconfigurable hardware or the evolutionary algorithm in place, but not both. EHW consists of two main components: a reconfigurable hardware core and an evolutionary algorithm. Evolvable hardware (EHW) is a powerful autonomous system for adapting and finding solutions within a changing environment.
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