title: Real-time neural network inversion on the SRC-6e reconfigurable computer.
abstract: 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.
authors.affiliation: Department of Electrical and Computer Engineering, Baylor University, Waco, TX 76798, USA. Russell_W_Duren@baylor.edu
authors.forename: Russell W
authors.initials: RW
authors.lastname: Duren
authors.forename: Robert J
authors.initials: RJ
authors.lastname: Marks
authors.forename: Paul D
authors.initials: PD
authors.lastname: Reynolds
authors.forename: Matthew L
authors.initials: ML
authors.lastname: Trumbo
mesh_terms: D001185:Artificial Intelligence; D003198:Computer Simulation; D003199:Computer Systems; D003201:Computers; D003205:Computing Methodologies; D004867:Equipment Design; D019544:Equipment Failure Analysis; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D012815:Signal Processing, Computer-Assisted / Q000295:instrumentation*