A Pre-Routing Net Wirelength Prediction Method Using an Optimized Convolutional Neural Network
A Pre-Routing Net Wirelength Prediction Method Using an Optimized Convolutional Neural Network
复制标题
一种利用优化卷积神经网络的预布线网络线长预测方法
DOI：
10.1109/candarw.2019.00028
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Takaichi Yoshida
中科院分区：
文献类型：
--
作者：
Ryota Watanabe;Yuki Katsuda;Qian Zhao;Takaichi Yoshida
电路实现的总线长是评估FPGA设计流程质量的重要指标。电路的所有网络的布线由布线确定，但是布线前阶段（如布局）可以使用线长预测模型来指导具有用于布线的较短总线长的布局解决方案的生成。传统的VPR采用基于边界框大小和网络的汇点数量的线长预测模型，该模型对于规则的2D阵列结构的FPGA工作良好。然而，新的FPGA架构，如3D-FPGA和分层路由不能使用这样一个简单的模型。在这项工作中，我们提出了一种使用卷积神经网络构建优化的网线长预测模型的方法，该方法可以从路由网络中学习路由特征，而无需手动调整。评估结果表明，优化的CNN模型也具有比VPR模型更高的准确性。
The total wirelength of a circuit implementation is an important metric to evaluate the quality of an FPGA design flow. The wirelengths of all nets of a circuit are determined by routing, but pre-routing stages like placement can use a wirelength prediction model to direct the generation of a placement solution with a shorter total wirelength for routing. The conventional VPR employs a wirelength prediction model based on the bounding box size and the number of sinks of a net, which works well for an FPGA of a regular 2D array structure. However, new FPGA architectures like 3D-FPGA and hierarchical routing cannot use such a simple model. In this work, we propose a method to build an optimized net wirelength prediction model using a convolutional neural network, which can learn routing features from routed nets without manual tunings. The evaluation results show an optimized CNN model also has higher accuracy than the VPR model.