Applications of Artificial Intelligence on the Modeling and Optimization for Analog and Mixed-Signal Circuits A Review
Abstract : Recently, there have been many studies attempting to take advantage of advancements in Artificial Intelligence (AI) in Analog and Mixed-Signal (AMS) circuit design. Automated circuit sizing optimization and improving the accuracy of performance models are the two predominant uses of AI in AMS circuit design. This paper first introduces and explains the basic concepts in AI especially the ones that are more suitable to this application. Next, it surveys some recent studies of various AI techniques for AMS circuit design. Then, it discusses the main approaches as well as the pros and cons of each method. Finally, it gives meaningful insights about the current challenges and open issues, as well as recommends approaches for specific applications.
? Existing formal methods provide appealing completeness and reliability, yet they suffer from their limited efficiency and scalability.
? The objective of this section is to provide definitions of different formal verification methods and a brief overview of some existing methods.
? Based on the implicit mapping mechanism, we propose to construct a new kernel by atomizing the existing kernel functions to achieve implicit feature weighting.
? Existing formal methods suffer from their limitation in efficiency and scalability while traditional simulation based methods rarely provide acceptable completeness or coverage.
? If a system mastering model returns a faulty prediction then the programmer needs to restore that problem explicitly however within the case of deep mastering, the version does it by himself.
? Issue is the highly dependent on which the datasets the data is trained and tested.
? By analyzing billions of knowledge points from previous outputs, we will predict the impact of bugs, design complexity, human resources, licenses, and compute farm throughput on current projects.
? It can be used to predict the impact of changes that is to understand that how the dependent variable changes when we change independent variable.
• The proposed modeling method and the hierarchical parallelization enhance the efficiency and scalability of reachability analysis for AMS verification.
• In addition, an asymmetric extension of the active learning framework is proposed to achieve conservative prediction by adding safety level evaluation of the model into the active learning strategy.
• Symbolic analysis was the first step towards circuit design as well as formal verification by nature, and numerous proof based symbolic methods have been proposed in the AMS verification domain.
• To achieve a less complex model, an abstraction method is proposed to model AMS systems with pure analog representations.
? Is used to explain the performance of a model. It provides a key role in the development of a model as it provides the insight to areas that require improvement.
? Moreover, major parameters like power efficiency, delay and accuracy is being taken into consideration for verifying the result.
? Ordinary Least square it tries to estimate the value of the coefficient by minimizing thee MSE.
? Optimization algorithm: to find the best parameters that is you can use a process of optimizing the value of the coefficient by iteratively minimizing the error of the model on your training data.
? Machine learning can be used for reduction of time for the design and simulation through existing complex algorithms.
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