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首页> 外文期刊>Chinese Journal of Electronics >Feedforward Neural Network Models for FPGA Routing Channel Width Estimation
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Feedforward Neural Network Models for FPGA Routing Channel Width Estimation

机译：用于FPGA路由通道宽度估计的前馈神经网络模型

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摘要

Since interconnects play the increasingly important role in delay and area of the Fieldprogrammable gate array (FPGA) implementations, routing architecture design has become the focus of much work related to FPGA architecture development. This paper leverages feedforward neural networks to derive accurate models of the routing channel width in homogeneous FPGA architecture with two advanced intelligence learning techniques: Gradient-based learning algorithm (GLA) and Extreme learning machine (ELM). The resultant models can be used in the early stages of FPGA architecture development to facilitate fast design space exploration which is difficult to achieve in the traditional experiment-based method. The proposed models are evaluated by comparing the estimated channel widths to the real values generated from a CAD tool VTR over IWLS2005 benchmark circuits. Results show that the GLA model achieves the estimation accuracy 3.98% and the ELM model has the accuracy 3.91%, which show significant improvement over existing estimation approaches.
机译：由于互连在现场可编程门阵列（FPGA）实现的延迟和面积方面起着越来越重要的作用，因此路由架构设计已成为与FPGA架构开发相关的许多工作的重点。本文利用前馈神经网络通过两种先进的智能学习技术，在同类FPGA体系结构中得出路由通道宽度的准确模型：基于梯度的学习算法（GLA）和极限学习机（ELM）。生成的模型可用于FPGA体系结构开发的早期阶段，以促进快速的设计空间探索，而这在传统的基于实验的方法中很难实现。通过将估计的通道宽度与IWLS2005基准电路上的CAD工具VTR生成的实际值进行比较，来评估所提出的模型。结果表明，GLA模型的估计精度为3.98％，ELM模型的精度为3.91％，与现有的估计方法相比有明显的改进。

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