Dynamic ML Placement of Computational Graph Operations
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Solution Overview
Problem
Conventional methods for assigning computational graph operations across computing devices in a computational environment are inefficient, as they rely on precomputed heuristics and predefined rules, failing to adapt dynamically to changing constraints and optimization goals, leading to suboptimal resource utilization and execution performance.
Innovation Solution
A machine learning-based system that processes context information and computational graph data to dynamically assign operations across devices, using a trained model to predict optimal placements that balance resource usage and optimization goals such as latency, energy impact, and computational time, without relying on pre-defined heuristics or rules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If precomputed heuristics and predefined rules are used for assigning computational graph operations, then the system complexity is reduced and ease of operation is improved, but the adaptability to changing constraints and optimization goals deteriorates
Solution Approach 1:
The patent implements dynamic placement assignment using machine learning models that continuously adapt to changing computational environments. The system evaluates current context information (device states, network conditions, optimization goals) and generates placement assignments in real-time, transforming the static heuristic-based system into a dynamic one that can respond to environmental changes while maintaining operational simplicity through automated decision-making.
Solution Approach 2:
The system changes the parameters used for placement decisions from fixed precomputed values to dynamic parameters generated by machine learning models. These parameters include context information about computing devices, network conditions, and optimization goals, allowing the system to adapt to varying conditions while presenting a simplified interface to users through automated policy management.
2Device complexity
If precomputed heuristics and predefined rules are used for assigning computational graph operations, then the device complexity is reduced, but the productivity and execution performance deteriorate due to suboptimal resource utilization
Solution Approach 1:
The patent introduces machine learning models as intermediaries between the computational graph operations and the computing devices. These models process context information and generate optimized placement assignments, acting as a smart mediator that improves resource utilization and execution performance without significantly increasing the complexity of individual devices, as the complexity is distributed across the centralized or federated learning system.
Solution Approach 2:
The system implements feedback loops where placement assignments are continuously evaluated based on actual execution performance and environmental changes. This feedback is used to retrain and refine the machine learning models, improving productivity over time while maintaining manageable device complexity through iterative optimization rather than complex static rules.
3Adaptability or versatility
If dynamic machine learning-based placement assignment is implemented, then the adaptability to changing constraints and optimization goals is improved, but the system complexity and computational overhead increase
Solution Approach 1:
The patent segments the placement assignment system into distinct components: context information collection, machine learning model processing, and placement assignment generation. This segmentation allows the complex adaptive functionality to be distributed across multiple modules, reducing the complexity burden on any single device while maintaining overall system adaptability through coordinated operation of the segmented components.
4Ease of operation
If conventional methods are used for assigning computational graph operations, then the system is easier to operate with predefined rules, but the loss of time occurs due to inability to quickly adapt to environmental changes
Solution Approach 1:
The system performs preliminary actions by pre-training machine learning models offline using historical data and simulated environments. This preliminary training enables the models to quickly adapt to new conditions when deployed, reducing the time needed for real-time decision-making while maintaining ease of operation through automated model selection and deployment without requiring users to manually adjust parameters during runtime.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for assigning operations of a computational graph to a plurality of computing devices are disclosed. Data characterizing a computational graph is obtained. Context information for a computational environment in which to perform the operations of the computational graph is received. A model input is generated, which includes at least the context information and the data characterizing the computational graph. The model input is processed using the machine learning model to generate an output defining placement assignments of the operations of the computational graph to the plurality of computing devices. The operations of the computational graph are assigned to the plurality of computing device according to the defined placement assignments.