1. 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.