Fast Dynamic IR-Drop Prediction Using Machine Learning in Bulk FinFET Technologies
Abstract
:1. Introduction
2. Proposed Technique
2.1. Proposed Flow
2.2. Nature Feature Extraction
2.3. Neighboring Feature Construction
2.4. Design Matrix Construction
2.5. Training Model
3. Experiments Setup
4. Results
4.1. IR-Drop Prediction before ECO
4.2. IR-Drop Prediction after ECO
4.3. Runtime Comparison
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Nature Features | Specifications |
---|---|
x, y | The x, y coordinate of an instance on layout design |
CF | The code that represents the cell function of an instance |
DS | The drive strength of an instance |
VTT | The voltage threshold type of an instance |
Rpu | The effective pull-up resistance of an instance |
Rpd | The effective pull-down resistance of an instance |
CL | The output load capacitance of an instance |
Tog | The signal toggle rate of an instance |
Il | The leakage current of an instance |
SIA | The total of scaled dynamic current and leakage current of an instance |
Ip | The peak current of an instance |
Pl | The leakage power of an instance |
SPi | The scaled internal power of an instance |
SPs | The scaled switching power of an instance |
SPA | The scaled total power of an instance |
Circuit Design | DesignA | DesignB | |||
---|---|---|---|---|---|
Power Analysis Mode | Vectorless | VCD | Vectorless | VCD1 | VCD2 |
Number of cell instance | 2.4 million | 2.4 million | 3.7 million | 3.7 million | 3.7 million |
Mean IRD (mV) | 15.31 | 2.19 | 16.84 | 3.54 | 8.42 |
Max IRD (mV) | 55.7 | 165.2 | 73.5 | 90.9 | 91.4 |
Number of IRD violations | 0 | 576 | 9 | 28 | 173 |
Partitions | Power Analysis Mode | Inst Numbers | Mean IRD (mV) | CC | MAE (mV) | MaxE (mV) | RMSE |
---|---|---|---|---|---|---|---|
P1 | vectorless | 545,772 | 15.97 | 0.976 | 0.845 | 15.49 | 1.14 |
VCD | 545,772 | 1.69 | 0.982 | 0.267 | 7.10 | 0.38 | |
P2 | vectorless | 444,511 | 14.69 | 0.982 | 0.784 | 16.03 | 1.07 |
VCD | 444,511 | 1.81 | 0.986 | 0.291 | 8.95 | 0.43 | |
P3 | vectorless | 590,385 | 16.03 | 0.974 | 0.847 | 16.55 | 1.14 |
VCD | 590,385 | 2.74 | 0.988 | 0.420 | 14.14 | 0.59 | |
P4 | vectorless | 460,383 | 14.22 | 0.979 | 0.807 | 14.89 | 1.11 |
VCD | 360,383 | 2.43 | 0.992 | 0.333 | 10.88 | 0.49 |
Partitions | Power Analysis Mode | Inst Numbers | Mean IRD (mV) | CC | MAE (mV) | MaxE (mV) | RMSE |
---|---|---|---|---|---|---|---|
P1 | vectorless | 1,177,525 | 16.37 | 0.960 | 1260 | 21.53 | 1.67 |
VCD1 | 1,177,525 | 1.70 | 0.976 | 0.312 | 14.29 | 0.48 | |
VCD2 | 1,177,525 | 11.38 | 0.972 | 1116 | 21.44 | 1.57 | |
P2 | vectorless | 947,562 | 14.13 | 0.960 | 1087 | 21.92 | 1.47 |
VCD1 | 947,562 | 2.01 | 0.987 | 0.276 | 18.09 | 0.45 | |
VCD2 | 947,562 | 4.13 | 0.961 | 0.777 | 22.17 | 1.17 | |
P3 | vectorless | 782,721 | 16.81 | 0.963 | 1176 | 23.44 | 1.58 |
VCD1 | 782,721 | 6.87 | 0.978 | 0.766 | 23.12 | 1.12 | |
VCD2 | 782,721 | 9.63 | 0.980 | 1077 | 26.09 | 1.58 | |
P4 | vectorless | 807,029 | 20.42 | 0.981 | 1092 | 19.81 | 1.47 |
VCD1 | 807,029 | 4.87 | 0.974 | 0.652 | 23.79 | 0.96 | |
VCD2 | 807,029 | 7.84 | 0.965 | 0.923 | 25.60 | 1.31 |
Partitions | Power Analysis Mode | Inst Numbers | Mean IRD (mV) | CC | MAE (mV) | MaxE (mV) | RMSE |
---|---|---|---|---|---|---|---|
P1 | vectorless | 545,795 | 16.03 | 0.973 | 0.888 | 16.63 | 1.19 |
VCD | 545,795 | 1.74 | 0.970 | 0.320 | 20.53 | 0.52 | |
P2 | vectorless | 444,543 | 14.75 | 0.980 | 0.835 | 18.86 | 1.14 |
VCD | 444,543 | 1.80 | 0.979 | 0.324 | 13.3 | 0.53 | |
P3 | vectorless | 590,410 | 16.10 | 0.971 | 0.882 | 20.97 | 1.19 |
VCD | 590,410 | 3.98 | 0.904 | 1189 | 22.45 | 1.88 | |
P4 | vectorless | 460,400 | 14.12 | 0.975 | 0.887 | 16.30 | 1.20 |
VCD | 460,400 | 2.08 | 0.901 | 0.882 | 20.02 | 0.98 |
Partitions | Power Analysis Mode | Inst Numbers | Mean IRD (mV) | CC | MAE (mV) | MaxE (mV) | RMSE |
---|---|---|---|---|---|---|---|
P1 | vectorless | 1,185,523 | 16.25 | 0.882 | 2.347 | 32.91 | 2.99 |
VCD1 | 1,185,523 | 1.69 | 0.962 | 0.379 | 31.04 | 0.66 | |
VCD2 | 1,185,523 | 11.80 | 0.915 | 1955 | 35.96 | 2.88 | |
P2 | vectorless | 959,577 | 14.15 | 0.908 | 2010 | 28.90 | 2.66 |
VCD1 | 959,577 | 1.79 | 0.964 | 0.404 | 30.57 | 0.78 | |
VCD2 | 959,577 | 4.46 | 0.945 | 0.887 | 34.47 | 1.36 | |
P3 | vectorless | 812,582 | 16.90 | 0.910 | 2370 | 36.53 | 3.11 |
VCD1 | 812,582 | 7.12 | 0.963 | 1091 | 37.70 | 1.68 | |
VCD2 | 812,582 | 11.01 | 0.970 | 1700 | 39.91 | 3.08 | |
P4 | vectorless | 829,071 | 20.63 | 0.929 | 2256 | 36.19 | 2.95 |
VCD1 | 829,071 | 5.07 | 0.935 | 1049 | 30.80 | 1.73 | |
VCD2 | 829,071 | 5.48 | 0.921 | 1485 | 39.50 | 1.49 |
Circuit Design | MAE (mV) | MaxE (mV) | CC | RMSE | TP | TN | FP | FN | Precision | Recall |
---|---|---|---|---|---|---|---|---|---|---|
DesignA (vectorlsess) | 0.875 | 20.97 | 0.975 | 1.181 | 0 | 2041148 | 0 | 0 | NAN | NAN |
DesignA (VCD) | 0.643 | 22.45 | 0.925 | 1.209 | 246 | 2040884 | 13 | 5 | 0.950 | 0.980 |
DesignB (vectorlsess) | 2.247 | 36.53 | 0.916 | 2.925 | 13 | 3786717 | 2 | 21 | 0.867 | 0.382 |
DesignB (VCD1) | 0.739 | 37.70 | 0.954 | 1.410 | 27 | 3786678 | 7 | 41 | 0.794 | 0.397 |
DesignB (VCD2) | 1.526 | 39.91 | 0.952 | 2.488 | 2137 | 3781511 | 143 | 2962 | 0.937 | 0.419 |
Circuit Design | DesignA (Vectorless) | DesignA (VCD) | DesignB (Vectorless) | DesignB (VCD1) | DesignB (VCD2) |
---|---|---|---|---|---|
Constructing training matrix | 23 min 22 s | 21 min 49 s | 24 min 7 s | 22 min 31 s | 22 min 43 s |
Training | 30 min 11 s | 30 min 7 s | 30 min 12 s | 30 min 8 s | 30 min 9 s |
Constructing predicting matrix | 1 h 11 min | 1 h 9 min | 1 h 36 min | 1 h 27 min | 1 h 27 min |
Predicting | 31 s | 30 s | 39 s | 38 s | 38 s |
IRD analysis by Redhawk | 5 h 41 min | 5 h 35 min | 6 h 57 min | 6 h 34 min | 6 h 44 min |
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Huang, P.; Ma, C.; Wu, Z. Fast Dynamic IR-Drop Prediction Using Machine Learning in Bulk FinFET Technologies. Symmetry 2021, 13, 1807. https://doi.org/10.3390/sym13101807
Huang P, Ma C, Wu Z. Fast Dynamic IR-Drop Prediction Using Machine Learning in Bulk FinFET Technologies. Symmetry. 2021; 13(10):1807. https://doi.org/10.3390/sym13101807
Chicago/Turabian StyleHuang, Pengcheng, Chiyuan Ma, and Zhenyu Wu. 2021. "Fast Dynamic IR-Drop Prediction Using Machine Learning in Bulk FinFET Technologies" Symmetry 13, no. 10: 1807. https://doi.org/10.3390/sym13101807
APA StyleHuang, P., Ma, C., & Wu, Z. (2021). Fast Dynamic IR-Drop Prediction Using Machine Learning in Bulk FinFET Technologies. Symmetry, 13(10), 1807. https://doi.org/10.3390/sym13101807