کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
462768 | 696898 | 2012 | 13 صفحه PDF | دانلود رایگان |
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 (O3) 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.
Journal: Microprocessors and Microsystems - Volume 36, Issue 2, March 2012, Pages 138–150