System and method for determining simulated response extrema for integrated circuit power supply networks
Systems and methods related to fast simulation of power supply networks and identification of a set of extrema (e.g., maxima or minima) waveforms associated with the power supply networks. In accordance with an embodiment, a method is provided for estimating the worst case voltage drop on the power delivery network of a circuit, comprising selecting a model of a power delivery network of a circuit, simulating the circuit over a predefined number of vectors, collecting dynamic voltage waveforms at each of a plurality of points on the power delivery network, calculating a dynamic worst case voltage waveform at each of the plurality of points, and reporting the dynamic worst case voltage waveform along with an associated confidence interval.
1. A method for estimating the worst case voltage fluctuation on the power delivery network of a circuit, comprising:
selecting a model of a power delivery network of a circuit;
simulating, by a computer, including a non-transitory computer readable storage medium and processor, the circuit over a predefined period of time;
collecting samples of dynamic voltage waveforms at each of a plurality of points on the power delivery network over the predefined period of time;
statistically estimating a worst-case dynamic voltage waveform at each of the plurality of points on the power delivery network based on the collected samples of dynamic voltage waveforms, resulting in a shift from the collected samples of dynamic voltage waveforms for the predefined period of time; and
reporting the statistical estimate of the worst-case dynamic voltage waveform at each point along with an associated confidence interval.
2. The method of claim 1 wherein the model is an R, RC, RL, RLC or RLCK model.
3. The method of claim 1 wherein the plurality of points include connections of digital gates to the power grid (i.e. ‘tap points’) and preselected points on the network.
4. The method of claim 1 wherein the predefined period of time can be varied depending on a required confidence interval for the simulation.
5. The method of claim 1 wherein extreme value theory is used to statistically estimate the worst-case dynamic voltage waveforms.
6. The method of claim 5 further comprising:
determining a set of extrema waveforms at each of the plurality of points.
7. The method of claim 6 further comprising:
performing transient analysis on each of the plurality of points based on the set of extrema waveforms using a linear network simulator.
8. The method of claim 7 wherein statistically estimating the worst-case dynamic voltage waveform includes computing a voltage drop for each transient analysis and determining a maximum value from among the computed voltage drops.
9. A system for estimating the worst case voltage fluctuation on the power delivery network of a circuit, comprising:
a computer, including a non-transitory computer readable storage medium and processor operating thereon;
a simulation tool, executing on the computer, wherein the simulation tool is operable to select a model of a power delivery network of a circuit,
simulate the circuit over a predefined period of time to achieve a particular accuracy,
collect samples of dynamic voltage waveforms at each of a plurality of points on the power delivery network over the predefined period of time,
statistically estimate a worst-case dynamic voltage waveform at each of the plurality of points on the power delivery network based on the collected samples of dynamic voltage waveforms, resulting in a shift of the collected samples of voltage waveforms for the predefined period of time, and
report the statistical estimate of the worst-case dynamic voltage waveform at each point along with an associated confidence interval.
10. The system of claim 9 wherein the model is an R, RC, RL, RLC or RLCK model.
11. The system of claim 9 wherein the plurality of points include tap points and preselected points on the network.
12. The system of claim 9 wherein the predefined period of time can be varied depending on a required confidence interval for the simulation.
13. The system of claim 9 wherein extreme value theory is used to statistically estimate the worst-case dynamic voltage waveforms.
14. The system of claim 13 further comprising:
determining a set of extrema waveforms at each of the plurality of points.
15. The system of claim 14 further comprising:
performing transient analysis on each of the plurality of points based on the set of extrema waveforms using a linear network simulator.
16. The system of claim 15 wherein statistically estimating the worst-case dynamic voltage waveform includes computing a voltage drop for each transient analysis and determining a maximum value from among the computed voltage drops.
17. A method for predicting extrema in time varying signals, comprising:
receiving, at a computer, including a non-transitory computer readable storage medium and processor, a model of a circuit wherein the model includes a plurality of cells;
simulating the model using a plurality of input vectors;
collecting dynamic waveform data for each of the plurality of cells;
using extreme value theory to statistically estimate a set of worst-case dynamic waveforms for each of the plurality of cells based on the collected samples of dynamic waveform data, resulting in a shift of the collected dynamic waveform data for the plurality of input vectors; and
reporting the statistical estimate of the set of worst-case dynamic waveforms for each of the plurality of cells.
18. The method of claim 17 further comprising:
determining a set of extrema waveforms at each of the plurality of cells.
19. The method of claim 18 further comprising:
performing analysis on each of the plurality of cells based on the set of extrema waveforms using a linear network simulator.