Intel corporation (20240135082). ENHANCED ARTIFICIAL INTELLIGENCE FOR PERFORMANCE VALIDATION OF CORE INTEGRAETED CIRCUIT FEATURES simplified abstract
Contents
ENHANCED ARTIFICIAL INTELLIGENCE FOR PERFORMANCE VALIDATION OF CORE INTEGRAETED CIRCUIT FEATURES
Organization Name
Inventor(s)
Kunapareddy Chiranjeevi of Hyderabad (IN)
Sakina Pitalwala of Bangalore (IN)
Karthik Varadarajan Rajagopal of Bangalore (IN)
ENHANCED ARTIFICIAL INTELLIGENCE FOR PERFORMANCE VALIDATION OF CORE INTEGRAETED CIRCUIT FEATURES - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240135082 titled 'ENHANCED ARTIFICIAL INTELLIGENCE FOR PERFORMANCE VALIDATION OF CORE INTEGRAETED CIRCUIT FEATURES
Simplified Explanation
The patent application describes a system that uses artificial intelligence to validate the performance of integrated circuit features. Here is a simplified explanation of the abstract:
- Extract source and destination registers from instruction files
- Generate a dependency graph with macroinstructions as nodes and dependencies as edges
- Create a frequency distribution of instructions from trace files
- Predict ratios of performance stats using machine learning
- Recommend traces for debugging based on predicted ratios
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- Potential Applications of this Technology
This technology could be applied in the semiconductor industry for validating the performance of integrated circuit features, optimizing instruction sets, and improving overall system efficiency.
- Problems Solved by this Technology
This technology helps in identifying performance bottlenecks, debugging issues related to integrated circuit features, and enhancing the overall functionality of the system.
- Benefits of this Technology
The benefits of this technology include improved performance validation, enhanced system efficiency, optimized instruction sets, and streamlined debugging processes.
- Potential Commercial Applications of this Technology
A potential commercial application of this technology could be in the development of advanced semiconductor products, optimizing performance in data centers, and improving the efficiency of computing systems.
- Possible Prior Art
One possible prior art in this field could be the use of machine learning algorithms for performance optimization in integrated circuits. Another could be the application of dependency graphs for analyzing system dependencies.
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- Unanswered Questions
- How does the system handle complex dependencies between macroinstructions?
The system uses a sophisticated algorithm to analyze and predict ratios of performance stats, but the exact method for handling complex dependencies between macroinstructions is not detailed in the abstract.
- What is the accuracy rate of the machine learning model in predicting ratios of performance stats?
While the abstract mentions the use of a machine learning model for predicting ratios, it does not provide information on the accuracy rate of these predictions. This could be crucial in determining the reliability of the system.
Original Abstract Submitted
this disclosure describes systems, methods, and devices related to using artificial intelligence to validate performance of integrated circuit features. a device may extract, from instruction files, microinstructions source and destination registers; generate a dependency graph including macroinstructions as nodes and dependencies between macroinstructions as edges between the nodes; generate, based on the dependency graph, a frequency distribution of instructions from trace files, performance univariate autoregressive conditionally heteroscedastic (perf uarch) stat files, and register transfer language (rtl) stat files, predictors for a machine learning model; generate, based on the perf uarch stat files and the rtl stat files, ratios of perf uarch stats to rtl stats as target stat ratios; generate, using the predictors and the machine learning model, predicted ratios of perf uarch stats to rtl stats; and generate, using greedy constrained optimization, based on the target stat ratios and the predicted ratios, recommended traces for debugging.
