MACHINE LEARNING IN ASIC BACKEND DESIGN Bowen Li, Paul Franzon Build Models for Physical Design. Use Machine Learning methods to predict the circuit performance after Physical Design Speedup design time. Objectives Surrogate Modeling for Global Route There are many surrogate models and adaptive model builders for us to use. Overall, 16 model are tested. Root relative squared error(RRSE) is used to measure model accuracy. RRSE is relative to squared error compared to a simple predictor. RRSE < 0.5 is target. Several model performances are shown below. ANN-Genetic model shows the best performance.   area_trial TNS violating_path WNS x_neg_1_4 x_neg_5_8 x_pos_0_10 x_pos_11_20 hold_slack_trial power_trial anngenetic 0.000 0.242 0.383 0.076 0.496 0.295 0.136 0.139 1.001 0.922 ann 0.003 0.270 0.364 0.079 0.517 0.302 0.193 0.227 1.000 0.939 annfixed 0.267 0.411 0.090 0.536 0.201 0.181 1.002 0.948 kriginggenetic 0.001 0.311 0.459 0.072 0.591 0.382 0.269 0.275 1.101 0.942 rational 0.004 0.262 0.394 0.096 0.542 0.428 0.362 0.371 1.016 0.961 gpmlgenetic 0.310 0.392 0.521 0.439 0.366 0.376 0.929 lssvmgenetic 0.309 0.403 0.088 0.522 0.437 0.377 0.378 0.930 elm 0.322 0.400 0.093 0.523 0.448 0.373 0.936 kriging 0.338 0.461 0.176 0.612 0.405 0.297 0.307 1.003 In this research, we separate physical design modeling into two stages. In Stage 1, Surrogate Modeling(SUMO) would run Physical Design for thousands of times to get obtain enough inputs and outputs after Global Route (GR). During this process, SUMO will generate models for each outputs to predict GR results in the future. In Stage 2, thousands of GR results and Detailed Route (DR) results are set as inputs and outputs respectively in machine learning models. Framework of Physical Design Simulation Linear regression, neural networks and decision tree models are used in this part. Here are the model accuracies for different outputs: Power: Linear Regression, 99% accuracy. Area: Linear Regression, 99 % accuracy. Hold Slack: Neural Networks, 93% accuracy; Decision Tree (turn to classification problem), 99% accuracy. Number of DRC: Decision Tree (turn to classification problem), 82.82% accuracy. Machine Learning for Detailed Route We selected six important features and 4 circuit performances. Also10 global route results are chosen, because they are related to four circuit performances. Inputs and Outputs of Model Conclusion and Future Work Achieving a high quality physical design is difficult and time consuming Many input “knobs” to set Evaluating each knob requires 40 minutes Key to success was picking the correct model for each steps Future Work: Improve Machine Learning Algorithm to get better performance. Build classifier so don’t have to build a model for each design.