Multi-State Acquisition Function for Scalable Circuit Optimization
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Solution Overview
Problem
Existing acquisition functions in Bayesian optimization are intractable, inefficient, and do not scale well with dimensionality, leading to suboptimal performance in optimizing circuit device features.
Innovation Solution
A multi-state acquisition function engine with exploitive and explorative states is used, coupled with a genetic algorithm engine, to dynamically balance exploration and exploitation, facilitating efficient selection of samples for objective function evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional acquisition functions are used in Bayesian optimization, then the optimization process can be performed, but the computation is intractable and does not scale well with dimensionality
Solution Approach 1:
The acquisition function is segmented into multiple candidate acquisition functions, each suitable for different dimensional ranges. The system divides the optimization space into segments based on dimensionality and selects the appropriate acquisition function segment for each segment, making the overall system tractable and scalable.
Solution Approach 2:
The system changes the parameter of the acquisition function by selecting from multiple candidate functions with different mathematical formulations. Each candidate function is optimized for specific dimensional ranges, allowing the system to adapt parameters based on the current optimization dimensionality.
2Productivity
If approximation strategies are used to replace intractable quantities, then computation becomes more readily computable, but the method may not converge to a true value
Solution Approach 1:
The system incorporates feedback mechanisms where the performance and convergence behavior of approximation strategies are monitored. Based on this feedback, the system adjusts the approximation level or switches between different candidate acquisition functions to maintain both computational efficiency and convergence to true values.
Solution Approach 2:
The system dynamically adjusts the acquisition function selection based on the optimization progress and dimensionality. Instead of using a fixed approximation strategy, the system transitions between different candidate functions as the optimization progresses, maintaining both efficiency and accuracy.
3Adaptability or versatility
If Monte Carlo methods are used for acquisition function sampling, then the method is versatile and generally unbiased, but it is perceived as non-differentiable and inefficient for maximizing the acquisition function
Solution Approach 1:
The system merges Monte Carlo sampling with gradient-based optimization methods. By combining the versatility and unbiased nature of Monte Carlo methods with the efficiency of gradient-based maximization, the system achieves both versatility and computational efficiency in acquiring optimal samples.
Data Source
AI summary
A multi-state acquisition function engine is provided for enhancing operation of an optimization tool to optimize a circuit device feature. Running the acquisition function engine facilitates tailoring optimization samples for objective function evaluation. The process includes configuring input parameters for running a genetic algorithm engine based, in part, on a current acquisition function state to tune the genetic algorithm engine based on the state. Further, the process includes running the genetic algorithm engine for one or more generations using the configured input parameters to generate a plurality of candidate samples, and providing selected sample(s) of the plurality of candidate samples to the objective function for generating a respective fitness score for optimizing the circuit design feature. The process further includes selecting, based at least in part on the generated fitness score(s), a new acquisition function state of the acquisition function engine for a next iteration.