GPU-Accelerated Gate Simulation and Machine Learning for IC Crosstalk
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Solution Overview
Problem
Crosstalk timing signoff in integrated circuit (IC) designs is difficult to perform accurately due to the large number of nets and potential aggressor-victim combinations, leading to pessimistic timing analysis that can either be overly conservative or require additional margins, which is inefficient and potentially flawed.
Innovation Solution
A gate-level simulation using graphics processor units (GPUs) to determine aggressor/victim pairs and their features, followed by a machine learning environment to predict delta delays, thereby improving the accuracy and efficiency of crosstalk analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If static analysis is used to determine crosstalk, then analysis speed is improved, but timing accuracy deteriorates due to pessimistic assumptions
Solution Approach 1:
The patent performs preliminary action by conducting comprehensive simulations during the design phase to collect data about actual crosstalk behavior. This preliminary data collection enables the system to later make more accurate predictions without requiring slow comprehensive simulations during the final timing analysis phase.
Solution Approach 2:
The patent creates a simplified model (copy) of the complex IC design that captures essential crosstalk characteristics. This model is used for rapid analysis while being calibrated against actual simulation data, allowing the system to achieve both speed and accuracy.
2Measurement precision
If comprehensive simulations are performed to analyze all aggressor-victim pairs, then timing accuracy is improved, but computational time and resource consumption increase significantly
Solution Approach 1:
The patent segments the comprehensive analysis into two phases: a simulation phase that collects data from representative samples, and an analysis phase that uses this data to quickly evaluate all aggressor-victim pairs. This segmentation allows accurate timing analysis without requiring comprehensive simulations for every possible pair.
Solution Approach 2:
The patent changes the approach from simulating every possible aggressor-victim pair to using statistical parameters and machine learning models trained on simulation data. This parameter transformation enables rapid prediction of crosstalk effects without exhaustive simulation.
3Productivity
If blanket heuristics are applied to mitigate pessimism, then analysis time is reduced, but reliability deteriorates due to potential optimism or excessive pessimism
Solution Approach 1:
The patent implements feedback by using machine learning models that are continuously trained on simulation data to refine their predictions. This feedback loop ensures that the analysis remains reliable by adjusting predictions based on actual observed behavior, avoiding both excessive pessimism and unwarranted optimism.
Solution Approach 2:
The patent transitions from static blanket heuristics to dynamic, data-driven predictions that adapt to the specific characteristics of each aggressor-victim pair. This dynamic approach allows the system to adjust its analysis based on actual design conditions, improving both reliability and efficiency.
Data Source
AI summary
To facilitate crosstalk analysis for an IC design, a plurality of input vectors are input into a gate-level simulation. In response, the gate-level simulation determines timing windows for all nets within the IC design, may perform aggressor pruning, and may then determine and output aggressor/victim pairs and associated features for the IC design. This gate-level simulation may be accelerated utilizing one or more graphics processor units (GPUs). Additionally, the aggressor/victim pairs and associated features for the IC design are then input into a trained machine learning environment, which outputs predicted delta delays for each of the aggressor/victim pairs. In this way, crosstalk analysis may be performed more accurately and efficiently.