Dynamic Operational Parameter Identification for ML Inference Latency
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Solution Overview
Problem
Existing machine learning models face challenges in optimizing inference performance across new types of networks and hardware platforms, often resulting in suboptimal key performance indicators (KPIs) due to predefined operational parameters.
Innovation Solution
A method and system for dynamically identifying and applying operational parameters that optimize inference performance while meeting specified latency requirements, by concurrently performing inferences and optimizing parameters based on the machine learning model and computing device properties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If predefined operational parameters are used to tune machine learning model performance, then key performance indicators for well-known networks are optimized, but inference performance for new types of networks and hardware platforms deteriorates
Solution Approach 1:
The system dynamically identifies and applies operational parameters during inference execution rather than using static predefined parameters. The operational parameter identifier continuously monitors inference performance metrics and adjusts parameters in real-time based on actual hardware behavior and network characteristics, enabling adaptation to new platforms without manual reconfiguration.
Solution Approach 2:
The machine learning system performs self-optimization by automatically identifying operational parameters that improve inference performance. The operational parameter identifier autonomously analyzes performance data and selects optimal parameters without external intervention, allowing the system to adapt to new hardware platforms and network types independently.
2Loss of time
If maximum latency requirements are enforced to meet application deadlines, then inference timing is controlled, but inference performance optimization is compromised
Solution Approach 1:
The system dynamically balances latency requirements with performance optimization by continuously monitoring both timing metrics and performance indicators. The operational parameter identifier adjusts parameters in real-time to meet latency constraints while maximizing performance, rather than using fixed parameters that cannot adapt to changing timing requirements.
Solution Approach 2:
The system implements feedback mechanisms where performance metrics from inference execution are fed back to the operational parameter identifier. This feedback loop enables the system to learn from actual performance data and adjust operational parameters to simultaneously satisfy latency requirements and optimize performance indicators.
3Reliability
If operational parameters are optimized concurrently during inference execution, then performance metric targets are approached, but computational resources are consumed
Solution Approach 1:
The system merges inference execution with operational parameter identification into a concurrent process rather than sequential operations. The operational parameter identifier runs alongside the inference execution, utilizing computational resources efficiently by performing parameter optimization during inference time rather than requiring separate optimization phases.
Solution Approach 2:
The system maintains continuous useful action by concurrently executing inferences and identifying operational parameters without idle periods. The operational parameter identifier actively monitors and analyzes performance data during inference execution, ensuring that computational resources are continuously utilized for productive purposes rather than wasting time on sequential processing.
Data Source
AI summary
Certain aspects of the present disclosure provide techniques for concurrently performing inferences using a machine learning model and optimizing parameters used in executing the machine learning model. An example method generally includes receiving a request to perform inferences on a data set using the machine learning model and performance metric targets for performance of the inferences. At least a first inference is performed on the data set using the machine learning model to meet a latency specified for generation of the first inference from receipt of the request. While performing the at least the first inference, operational parameters resulting in inference performance approaching the performance metric targets are identified based on the machine learning model and operational properties of the computing device. The identified operational parameters are applied to performance of subsequent inferences using the machine learning model.