Decision making in synthesis cross technologies using LSTMs and transfer learning

Abstract

We propose a general approach that precisely estimates the Quality-of-Result (QoR), such as delay and area, of unseen synthesis flows for specific designs. The main idea is leveraging LSTM-based network to forecast the QoR, where the inputs are synthesis flows represented in novel timed-flow modeling, and QoRs are ground truth. This approach is demonstrated with 1.2 million data points collected using 14nm, 7nm regular-voltage (RVT), and 7nm low-voltage (LVT) technologies with twelve IC designs. The accuracy of predicting the QoRs (delay and area) evaluated using mean absolute prediction error (MAPE). While collecting training data points in EDA can be extremely challenging, we propose to elaborate transfer learning in our approach, which enables accurate predictions cross different technologies and different IC designs. Our transfer learning approach obtains estimation MAPE 3.7% over $sim:960,000 test points collected on 7nm technologies, with only 100 data points used for training the pre-trained LSTM network using 14nm dataset.