ML Runtime Estimator Selection for Accuracy-Cost Tradeoffs
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Solution Overview
Problem
Existing runtime performance estimators for autonomous vehicles vary in accuracy and cost, making it challenging to select an optimum estimator without incurring high computational expenses or risking safety due to inaccuracies.
Innovation Solution
A machine learning model is employed to estimate the accuracy and cost of different runtime performance estimators, considering safety metrics, to select an optimum estimator for a given compute workload and hardware configuration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a high-accuracy runtime performance estimator is used, then measurement precision is improved, but computational cost and time increase
Solution Approach 1:
The system employs multiple runtime performance estimators with varying accuracy levels and computational costs. Instead of always using the most accurate (and expensive) estimator, the system selectively applies different estimators based on the specific compute workload characteristics. This allows using simpler, faster estimators for workloads where high precision is less critical, while reserving high-accuracy estimators for workloads where precision is paramount.
Solution Approach 2:
The system changes the parameters of the estimation process by selecting different estimators based on workload parameters. The machine learning model analyzes workload characteristics (such as compute intensity, memory access patterns, hardware configuration) and adjusts the estimation approach accordingly, matching estimator complexity to workload requirements rather than using a fixed high-accuracy estimator for all cases.
2Productivity
If computational resources are reduced to lower cost, then productivity is improved, but measurement precision deteriorates
Solution Approach 1:
The system applies partial action by using only the level of estimation accuracy that is sufficient for each specific workload. Rather than always applying the most rigorous (and resource-intensive) estimation method, the machine learning model determines the appropriate estimation depth and accuracy level needed for each compute workload, avoiding unnecessary computational overhead while maintaining adequate precision.
Solution Approach 2:
The system dynamically adjusts estimation parameters based on workload characteristics. The machine learning model modifies the estimation approach by selecting different estimators or adjusting estimator parameters according to the specific compute workload and target hardware, optimizing the balance between precision and computational resource consumption for each case.
3Reliability
If multiple runtime performance estimators are evaluated, then reliability is improved, but device complexity increases
Solution Approach 1:
The system introduces a machine learning model as an intermediary that automatically selects the appropriate runtime performance estimator based on workload characteristics. This intermediary layer manages the complexity of having multiple estimators by providing an intelligent, automated selection mechanism that matches workloads to suitable estimators, reducing the need for manual configuration and simplifying the overall system architecture.
Solution Approach 2:
The system enables self-service by allowing the machine learning model to autonomously evaluate workload characteristics and select the most appropriate estimator without human intervention. The system automatically adapts to different compute workloads and hardware configurations, making the complexity management transparent and eliminating the need for manual estimator selection or configuration.
Data Source
AI summary
Systems and techniques are provided for selecting a runtime performance estimator. An example method includes receiving, by a machine learning model, at least one compute workload and a target hardware parameter, wherein the target hardware parameter identifies one or more hardware components configurable to execute the at least one compute workload; identifying a plurality of runtime performance estimators for obtaining a predicted performance of the at least one compute workload on the one or more hardware components; determining a plurality of accuracy parameters and a plurality of cost parameters that are associated with the predicted performance obtained from the plurality of runtime performance estimators; and selecting, based on the plurality of accuracy parameters and the plurality of cost parameters, a preferred runtime performance estimator from the plurality of runtime performance estimators for obtaining the predicted performance of the at least one compute workload using the one or more hardware components.