Duren Russell W, Marks Robert J, Reynolds Paul D, Trumbo Matthew L
Department of Electrical and Computer Engineering, Baylor University, Waco, TX 76798, USA.
IEEE Trans Neural Netw. 2007 May;18(3):889-901. doi: 10.1109/TNN.2007.891679.
Implementation of real-time neural network inversion on the SRC-6e, a computer that uses multiple field-programmable gate arrays (FPGAs) as reconfigurable computing elements, is examined using a sonar application as a specific case study. A feedforward multilayer perceptron neural network is used to estimate the performance of the sonar system (Jung et al., 2001). A particle swarm algorithm uses the trained network to perform a search for the control parameters required to optimize the output performance of the sonar system in the presence of imposed environmental constraints (Fox et al., 2002). The particle swarm optimization (PSO) requires repetitive queries of the neural network. Alternatives for implementing neural networks and particle swarm algorithms in reconfigurable hardware are contrasted. The final implementation provides nearly two orders of magnitude of speed increase over a state-of-the-art personal computer (PC), providing a real-time solution.
以声纳应用作为具体案例研究，考察了在使用多个现场可编程门阵列（FPGA）作为可重构计算元件的SRC - 6e计算机上实现实时神经网络反演。使用前馈多层感知器神经网络来估计声纳系统的性能（Jung等人，2001年）。粒子群算法利用训练好的网络在存在既定环境约束的情况下搜索优化声纳系统输出性能所需的控制参数（Fox等人，2002年）。粒子群优化（PSO）需要对神经网络进行重复查询。对比了在可重构硬件中实现神经网络和粒子群算法的不同方法。最终实现的速度比最先进的个人计算机（PC）快近两个数量级，提供了一个实时解决方案。
