基于注意力机制的FPGA布线拥塞预测

Attention mechanism based routingcong estion predictionforFPGA

随着FPGA设计复杂性的不断增加,物理设计需要大量的优化迭代才能实现,布线拥塞影响芯片的面积及时延等性能指标,因此需要准确快速的预测并提前解决。提出一个FPGA布线拥塞预测模型CBAM-CGAN,模型在布局阶段提取特征合成学习图像,引入注意力机制学习增强图像各个特征通道的重要程度,提高布线拥塞的预测性能。实验结果表明,方法在布局阶段的布线拥塞预测取得了较好效果。相比于条件对抗生成网络模型,结构相似度平均值提高了0.89%,峰值信噪比平均值提高了1.37%,归一化均方根像素差平均值降低了3.8%,像素精度差平均值降低了0.06%,单张图像的预测时间约为0.1 s。实验数据证明了模型在FPGA布线拥塞的准确性和快速性。

With the increasing complexity of FPGA design,physical design requires a large number of optimization iterations to achieve.Cabling congestion affects chip area,delay and other performance indicators,so accurate and rapid prediction and early resolution are required.A FPGA routing congestion prediction model CBAM-CGAN is proposed.The model extracts feature in the layout phase to synthesize learning images,and introduces attention mechanism learning to enhance the importance of each feature channel of the image,so as to improve the routing congestion prediction performance.The experimental results show that the method achieves good results in routing congestion prediction in the layout phase.Compared with the conditional countermeasure generation network model,the average value of structure similarity is increased by 0.89%,the average value of peak signal to noise ratio is increased by 1.37%,the average value of normalized root mean square pixel difference is decreased by 3.8%,the average value of pixel accuracy difference is decreased by 0.06%,and the prediction time of a single image is about 0.1 seconds.Experimental data prove the accuracy and rapidity of the model in FPGA routing congestion.