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Intrinsic evolvable hardware platform for digital circuit design and repair using genetic algorithms

机译:使用遗传算法进行数字电路设计和修复的内在可扩展硬件平台

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A hardware/software platform for intrinsic evolvable hardware is designed and evaluated for digital circuit design and repair on Xilinx Field Programmable Gate Arrays (FPGAs). Dynamic bitstream compilation for mutation and crossover operators is achieved by directly manipulating the bitstream using a layered framework. Experimental results on a case study have shown that benchmark circuit evolution from an unseeded initial population, as well as a complete recovery of a stuck-at fault is achievable using this platform. An average of 0.47 μs is required to perform the genetic mutation, 4.2 μs to perform the single point conventional crossover, 3.1 μs to perform Partial Match Crossover (PMX) as well as Order Crossover (OX), 2.8 μs to perform Cycle Crossover (CX), and 1.1 ms for one input pattern intrinsic evaluation. These represent a performance advantage of three orders of magnitude over the JBITS software framework and more than seven orders of magnitude over the Xilinx design tool driven flow for realizing intrinsic genetic operators on Xilinx Virtex Family devices.
机译:针对Xilinx现场可编程门阵列(FPGA)上的数字电路设计和维修,设计并评估了用于内部可演化硬件的硬件/软件平台。通过使用分层框架直接操纵位流,可以实现针对变异和交叉算子的动态位流编译。案例研究的实验结果表明,使用该平台可以实现从无种子的初始种群中进行基准电路演进,以及完全恢复卡住的故障。进行基因突变平均需要0.47μs,执行单点常规交叉平均需要4.2μs,进行部分匹配交叉(PMX)和顺序交叉(OX)需要3.1μs,进行循环交叉(CX)需要2.8μs )和1.1 ms用于一种输入模式固有评估。与在JBITS软件框架上实现三个数量级的性能优势相比,在Xilinx设计工具驱动的流程上实现Xilinx Virtex系列设备上的内在遗传算子的性能优势而言,这些性能优势超过了七个数量级。

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