Integrated Circuit Optimization Modeling for Leakage Power Reduction
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Solution Overview
Problem
Current methods for optimizing integrated circuit designs, such as gate length adjustments, are resource-intensive and yield inconsistent results, making it difficult to predict the benefits of constrained optimization procedures before committing to expensive modifications.
Innovation Solution
A computer-implemented modeling methodology that predicts the susceptibility of circuit designs to optimization procedures, such as leakage power reduction, by analyzing design characteristics like logic depth and cell sensitivity, allowing for informed decision-making on resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If gate length adjustments or other circuit modifications are applied to optimize leakage power reduction, then leakage power is reduced, but the optimization procedures are resource-intensive and yield inconsistent results
Solution Approach 1:
The patent applies preliminary action by performing a predictive analysis before committing to expensive optimization procedures. The system analyzes design characteristics (logic depth, cell sensitivity, timing slack) to predict optimization benefits in advance, allowing designers to make informed decisions about whether to proceed with resource-intensive modifications. This prevents wasted resources on designs unlikely to benefit from optimization.
Solution Approach 2:
The patent changes parameters by introducing a predictive modeling approach that evaluates multiple design characteristics (logic depth, cell instance sensitivity, timing slack) to determine optimization suitability. Instead of blindly applying modifications, the system transforms the decision-making process by using quantitative parameters to predict outcomes, thereby improving design efficiency while targeting leakage power reduction only when beneficial.
2Reliability
If expensive optimization procedures are applied to all circuit designs, then some designs may achieve optimization benefits, but resource consumption increases substantially for designs that yield little improvement
Solution Approach 1:
The patent changes parameters by establishing a predictive framework that evaluates multiple design characteristics (logic depth, cell instance sensitivity, timing slack) to determine optimization suitability. Instead of blindly applying modifications, the system transforms the decision-making process by using quantitative parameters to predict outcomes, thereby improving design efficiency while targeting leakage power reduction only when beneficial.
Solution Approach 2:
The patent applies feedback by using the predictive analysis results to guide subsequent optimization decisions. The system provides feedback about which designs are likely to benefit from optimization procedures, allowing designers to adjust their resource allocation strategies accordingly. This feedback loop ensures that computing resources are directed toward designs with high predicted benefit while avoiding unnecessary processing for designs unlikely to improve.
3Manufacturing precision
If finer levels of granularity in gate length biasing are used, then delay-leakage trade-off precision is improved, but the complexity of the optimization process increases
Solution Approach 1:
The patent applies local quality by enabling selective application of different optimization granularities to different parts of the circuit design. The predictive analysis identifies specific cell instances and logic paths where fine-grained optimization would be most beneficial, allowing the system to apply detailed gate length adjustments only where needed rather than uniformly across the entire design. This reduces overall process complexity while maintaining precision where it matters most.
Data Source
AI summary
A design optimization method for a target circuit design specified by a machine-readable file, comprises providing a computer-implemented model as a function of a set of characteristics of circuit designs of circuit optimization achievable due to a circuit modification procedure, such as timing constrained gate length modification for leakage power reduction. Using values of said set of characteristics for the target circuit design, the computer-implemented model is applied to the target circuit design to produce an indication of susceptibility of the target circuit design to optimization. The model can be produced using Monte Carlo simulations of a set of virtual designs, and fitting a function of said characteristics to the results.