Improving Seed-Based FPGA Packing with Indirect Connection for Realization of Neural Networks

Abstract

FPGAs are gaining favor among researchers in fields including artificial intelligence and big data due to their configurability and high level of parallelism. As the packing methods indisputably affect the implementation performance of FPGA chips, packing techniques play an important role in the design automation flow of FPGAs. In this paper, we propose a quantitative rule for packing priority of neural network circuits, and optimize the traditional seed-based packing methods with special primitives. The experiment result indicates that the proposed packing method achieves an average decrease of 8.45% in critical path delay compared to the VTR8.0 on Koios deep learning benchmarks.