A scalable pipelined architecture for real-time computation of MLP-BP neural networks

摘要

In this paper a novel architecture for implementing multi-layer perceptron (MLP) neural networks on field programmable gate arrays (FPGA) is presented. The architecture presents a new scalable design that allows variable degrees of parallelism in order to achieve the best balance between performance and FPGA resources usage. Performance is enhanced using a highly efficient pipelined design. Extensive analysis and simulations have been conducted on four standard benchmark problems. Results show that a minimum performance boost of three orders of magnitude (O~3) over software implementation is regularly achieved. We report performance of 2-67 GCUPS for these simple problems, and performance reaching over 1 TCUPS for larger networks and different single FPGA chips. To our knowledge, this is the highest speed reported to date for any MLP network implementation on FPGAs.