Knowledge-Based Neural Network Model for FPGA Logical Architecture Development

Abstract

This paper proposes a knowledge-based neural network (KBNN) modeling approach for field-programmable gate array (FPGA) logical architecture design. The KBNN embeds the existing FPGA analytical models (AMs) into an NN. The NN can complement the AMs according to their needs to provide further increased model accuracy, while maintaining the meaningful trends successfully captured in the AMs. The obtained KBNN predicts the routing channel width required by circuit implementations on various FPGA architectures, which can be used by architects to quickly and accurately evaluate various FPGA architectures in early development stages. Experimental results show that the KBNN-based approach achieves an average error of 2%, which shows 75% accuracy enhancement over the existing AMs for routing channel width estimation of a set of benchmark circuits and FPGA architectures. The KBNN model has been applied to three FPGA architecture development scenarios to demonstrate its practical application and effectiveness.