A Lightweight Inception Boosted U-Net Neural Network for Routability
  Prediction
Hailiang Li Yan Huo Yan Wang Xu Yang Miaohui Hao Xiao Wang
As the modern CPU, GPU, and NPU chip design complexity and transistor counts
keep increasing, and with the relentless shrinking of semiconductor technology
nodes to nearly 1 nanometer, the placement and routing have gradually become
the two most pivotal processes in modern very-large-scale-integrated (VLSI)
circuit back-end design. How to evaluate routability efficiently and accurately
in advance (at the placement and global routing stages) has grown into a
crucial research area in the field of artificial intelligence (AI) assisted
electronic design automation (EDA). In this paper, we propose a novel U-Net
variant model boosted by an Inception embedded module to predict Routing
Congestion (RC) and Design Rule Checking (DRC) hotspots. Experimental results
on the recently published CircuitNet dataset benchmark show that our proposed
method achieves up to 5% (RC) and 20% (DRC) rate reduction in terms of
Avg-NRMSE (Average Normalized Root Mean Square Error) compared to the classic
architecture. Furthermore, our approach consistently outperforms the prior
model on the SSIM (Structural Similarity Index Measure) metric.