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There are many unique challenges associated with providing remote access to space experimental payloads. The limited bandwidth to the space craft, the inability to physically monitor and probe the payload, and the management of access time for various researchers working on the project all compound to create a challenging work environment. The Configurable Fault Tolerant Processor (CFTP) project aims to alleviate many of the difficulties associated with remote payload operation. We have made use of modular FPGA design, which allows us to transfer only small application modules rather than full configuration files. This dramatically reduces the bandwidth required to upload new applications as we discover new experiments for the CFTP after launch. Another unique aspect of the CFTP project is the collaborative effort in its development. We must manage access time for universities and research institutions across the country for running experiments on the CFTP, downloading CFTP documents, and analyzing telemetry after launch.

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.Index Terms-Field-programmable gate arrays (FPGAs), inverse problems, neural network hardware, particle swarm theory, real-time systems, reconfigurable architectures, sonar.

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