On-Chip Neural Network Profiling for Runtime Bottleneck Detection
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Solution Overview
Problem
Existing technologies lack effective methods for accurately profiling and managing the on-chip performance of neural network execution, particularly in identifying deviations and bottlenecks at various levels of granularity, which hinders optimization and efficiency.
Innovation Solution
A system and method for profiling neural network performance that involves generating expected performance metrics based on a static schedule, capturing runtime performance metrics, and comparing them to detect deviations, enabling real-time adjustments and visualizations to optimize neural network execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-time performance monitoring is implemented for neural network execution, then performance measurement accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments performance monitoring into distinct components: static schedule analysis, runtime metric collection, deviation detection, and notification generation. Each component handles a specific aspect of performance profiling, making the overall system more manageable and less complex while maintaining measurement accuracy.
Solution Approach 2:
The patent introduces an intermediary profiling system that sits between the neural network execution and the user interface. This intermediary automatically collects runtime metrics, compares them against static schedules, and generates notifications, thereby improving measurement accuracy without directly increasing the complexity of the core neural network execution system.
2Productivity
If runtime performance metrics are continuously monitored and compared against static schedules, then performance management effectiveness is improved, but processing overhead increases
Solution Approach 1:
The system performs preliminary analysis by establishing static schedules and expected performance metrics before neural network execution begins. This pre-computation of baseline expectations allows runtime monitoring to focus only on detecting deviations rather than analyzing all performance data from scratch, reducing processing overhead while maintaining effective performance management.
Solution Approach 2:
The patent implements a feedback mechanism where runtime performance metrics are continuously compared against static schedules, and notifications are generated only when deviations are detected. This selective feedback approach improves performance management effectiveness by focusing attention on actual problems while minimizing unnecessary processing overhead from continuous full-system analysis.
3Measurement precision
If detailed performance metrics are collected and analyzed, then bottleneck identification accuracy is improved, but data processing requirements increase
Solution Approach 1:
The system extracts only the critical performance metrics necessary for bottleneck identification from the full set of available runtime data. By selecting and monitoring specific key performance indicators against the static schedule, the system achieves accurate bottleneck identification without the need to process and analyze all possible performance data, thereby reducing data processing requirements.
Data Source
AI summary
A method includes: accessing a static schedule of a target neural network for execution by a processing device, the target neural network including a set of layers; generating a set of expected performance metrics of the target neural network based on the static schedule, the set of expected performance metrics including a first expected performance metric for a first layer in the set of layers; accessing a set of runtime performance metrics captured during execution of the target neural network by the processing device, the set of runtime performance metrics including a first runtime performance metric for the first layer; and, in response to detecting a difference between the first runtime performance metric and the first expected performance metric exceeding a threshold, serving an alert at a user interface.