ML Model Orchestration via Synchronization Timing
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Solution Overview
Problem
Existing machine learning (ML) models, particularly neural networks and convolutional neural networks, face challenges in efficiently executing on devices with limited compute and memory resources, necessitating optimization techniques to align resource usage and improve performance.
Innovation Solution
The technique involves simulating ML model execution on target hardware to determine resource requirements and generate synchronization information, allowing for delayed execution to optimize resource allocation and balance peak resource consumption across multiple cores, using a leaky bucket optimization scheme to dynamically adjust timing and synchronization points.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple ML models are executed concurrently on multi-core hardware, then processing throughput is improved, but peak resource consumption increases requiring additional hardware resources
Solution Approach 1:
The system performs preliminary simulation of ML model execution to determine resource requirements and timing information before actual deployment. This allows the orchestration system to pre-calculate optimal scheduling strategies that balance concurrent execution with resource consumption, enabling throughput improvement without proportionally increasing hardware resources.
Solution Approach 2:
The patent implements dynamic scheduling where the orchestration system adjusts the execution timing of multiple ML models based on simulated resource consumption patterns. By dynamically optimizing the launch timing of models, the system can maximize concurrent processing throughput while keeping peak resource consumption within acceptable limits, resolving the contradiction between productivity and resource quantity.
2Speed
If ML models are executed without synchronization, then execution speed is improved, but resource allocation becomes unbalanced causing inefficiency
Solution Approach 1:
The system performs preliminary simulation to determine optimal timing information for each ML model execution. This pre-calculated timing data is then used to schedule model launches in a way that balances resource consumption across time, ensuring both fast execution and efficient resource allocation without requiring complex runtime synchronization mechanisms.
Solution Approach 2:
The patent changes the timing parameter of model execution based on simulated resource consumption patterns. By adjusting launch times and execution schedules according to pre-determined timing information, the system achieves balanced resource allocation while maintaining high execution speed, resolving the contradiction between speed and productivity.
3Reliability
If hardware resources are increased to handle peak consumption, then reliability is improved, but device complexity and cost increase
Solution Approach 1:
The system performs preliminary simulation of ML model execution patterns to predict peak resource consumption scenarios. This allows the orchestration system to implement scheduling strategies that prevent resource exhaustion without requiring over-provisioned hardware, thereby maintaining reliability while avoiding increased device complexity and cost.
Solution Approach 2:
The patent implements dynamic timing optimization that adapts resource usage patterns over time. By dynamically adjusting model execution schedules based on simulated consumption patterns, the system ensures reliable resource availability without requiring static over-provisioning of hardware, thus avoiding increased device complexity.
Data Source
AI summary
Techniques for executing machine learning (ML) models including receiving an indication to run a ML model, receiving synchronization information for organizing the running of the ML model with other ML models, determining, based on the synchronization information, to delay running the ML model, delaying the running of the ML model, determining, based on the synchronization information, a time to run the ML model; and running the ML model at the time.