An implementation of network learning on the Connection Machine
Connectionist models and their implications: readings from cognitive science
Systolic Implementation of a Pipelined On-Line Backpropagation
MICRONEURO '99 Proceedings of the 7th International Conference on Microelectronics for Neural, Fuzzy and Bio-Inspired Systems
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The paper describes the implementation of a systolic array for a multilayer perceptron on different FPGA architectures with a hardware-friendly learning algorithm: Pipelined On-line Backpropagation. By exploiting the embedded memories of certain families alongside the projection used in the systolic architecture, we can implement very large interconnection layers. These physical and architectural features - together with the combination of FPGA reconfiguration properties with a design flow based on generic VHDL - permit us to create an easy, flexible and fast method of designing a complete ANN on a single FPGA. The result offers a high degree of parallelism and fast performance.