Deep Learning Compiler Performance Monitoring With AI Optimizer Feedback
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Solution Overview
Problem
Conventional deep learning compilers lack continuous monitoring and management of optimization performance, particularly in AI-based optimizers, leading to issues such as unmonitored performance degradation, lack of notification for suboptimal performance, and inability to identify bottlenecks or resource inefficiencies.
Innovation Solution
A system comprising a metric module, simulation module, and monitoring module to calculate and analyze performance metrics, score function values, and provide performance analysis and notification, using reinforcement learning to optimize resource policies and hardware design.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If reinforcement learning is employed to optimize resource policies in deep learning compilers, then optimization performance is improved, but performance degradation cannot be monitored and managed
Solution Approach 1:
The patent implements a feedback mechanism by continuously monitoring compiler performance metrics and comparing them against expected performance levels. The system detects performance degradation, identifies its causes, and triggers notifications or retraining processes, forming a closed-loop control system that maintains reliability while preserving the productivity gains from reinforcement learning optimization.
Solution Approach 2:
The patent introduces an intermediary monitoring system that acts as a mediator between the reinforcement learning optimizer and the compiler performance. This intermediary layer collects metrics, analyzes performance deviations, and facilitates communication between different system components, enabling reliable monitoring without interfering with the optimization process itself.
2Productivity
If AI-based optimizers are used to generate instructions for AI accelerators, then resource optimization is improved, but performance issues cannot be detected or managed
Solution Approach 1:
The patent segments the performance monitoring task into distinct components: metric collection, performance calculation, expected performance determination, and degradation detection. By dividing the monitoring system into these functional modules, each handling a specific aspect of performance analysis, the system makes complex AI optimizer performance measurable and manageable through structured, modular processing.
Solution Approach 2:
The patent replaces manual performance analysis with an automated monitoring system that uses metric modules to collect data, calculation modules to compute performance indicators, and analysis modules to detect degradation. This substitution of automated computational processes for manual inspection enables continuous, precise detection of performance issues in AI-based optimizers.
3Productivity
If deep learning compilers perform scheduling optimization for limited hardware resources, then execution efficiency is improved, but performance degradation goes unnoticed
Solution Approach 1:
The patent establishes a feedback loop that continuously collects execution metrics from the deep learning compiler, compares actual performance against expected performance thresholds, and provides information about performance degradation. This feedback mechanism ensures that valuable performance information is captured and communicated, enabling timely responses to efficiency losses.
Solution Approach 2:
The patent implements continuous performance monitoring that operates throughout the compiler's execution lifecycle. Rather than periodic or post-hoc analysis, the monitoring system continuously collects metrics and detects degradation in real-time, ensuring that performance information is maintained without interruption and enabling continuous optimization adjustments.
Data Source
AI summary
Disclosed are an apparatus and method for monitoring the optimization performance of a deep learning compiler. The method includes calculating metric information for the evaluation of the performance of a compiler, calculating a score function value corresponding to resource optimization policy information, set in the artificial intelligence (AI)-based optimizer of the compiler, based on the metric information, and providing performance analysis results of the AI-based optimizer based on the score function value.