AI Validation for Integrated Circuit Performance Bugs
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Solution Overview
Problem
Current manual processes for validating integrated circuit performance are time-consuming, prone to duplicating effort, and may miss real performance issues due to the complexity of analyzing large numbers of traces and statistical values, leading to inefficiencies and potential product quality issues.
Innovation Solution
An AI-based approach employing algorithms like Page Rank, Random Forest, and Greedy constrained optimization to identify unique traces impacted by bugs, using machine learning models trained on predictors such as instruction frequencies, dependencies, and knob values to recommend traces for debugging, thereby improving the efficiency and accuracy of performance validation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual analysis of traces is used for performance validation, then human expertise can be applied to complex data, but the process becomes time-consuming and inefficient
Solution Approach 1:
The patent replaces manual mechanical analysis of traces with an AI-based automated system. The machine learning model automatically processes trace data, identifies performance issues, and generates validation reports, eliminating the need for manual trace analysis while maintaining validation accuracy through intelligent pattern recognition and anomaly detection algorithms.
Solution Approach 2:
The validation system performs self-service by automatically analyzing its own trace data without human intervention. The AI model independently processes performance metrics, identifies bugs, and generates validation results, enabling the system to validate itself efficiently at scale without requiring manual expertise for each validation case.
2Measurement precision
If manual trace analysis is performed, then detailed inspection can be conducted, but duplicate effort occurs and resources are wasted
Solution Approach 1:
The patent implements feedback mechanisms where the AI model continuously learns from validation results and trace data patterns. The system provides feedback by automatically identifying duplicate issues, learning from previous validation outcomes, and adjusting its analysis to focus only on unique problems, thereby eliminating redundant manual inspection effort while maintaining high detection accuracy.
Solution Approach 2:
The system extracts only the most critical and unique information from trace data using AI-based filtering and prioritization. By extracting only the essential performance anomalies and unique bugs rather than analyzing every trace detail manually, the system reduces validation time and resource consumption while maintaining measurement precision through intelligent data selection and prioritization.
3Reliability
If comprehensive trace analysis is conducted, then all performance issues can be identified, but the complexity of analysis increases
Solution Approach 1:
The patent segments the complex trace analysis into distinct manageable components: performance metric extraction, anomaly detection, bug identification, and validation reporting. Each segment is handled by specialized AI modules that process specific aspects of the data independently, reducing overall analysis complexity while maintaining comprehensive coverage for high product quality through systematic decomposition of the validation task.
Data Source
AI summary
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.