Topology-Aware Deep Learning Inference Tuning
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Solution Overview
Problem
Conventional deep learning approaches perform hyperparameter tuning exclusively during the training phase, which can limit model performance due to topology indifference, failing to adapt to varying hardware configurations during the inference phase.
Innovation Solution
The method involves detecting topological information related to hardware topology during the inference phase and automatically selecting hyperparameters based on this information, allowing for topology-aware tuning of deep learning models to optimize performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If the same set of hyperparameters is utilized across multiple models regardless of differing topologies, then implementation complexity is reduced, but model performance is limited and reduced
Solution Approach 1:
The patent applies local quality by selecting hyperparameters specific to each model's topology characteristics rather than using uniform hyperparameters across all models. The system analyzes individual model topologies and assigns optimized hyperparameter sets tailored to each model's structural properties, thereby improving performance while managing complexity through automated selection.
Solution Approach 2:
The patent implements parameter changes by dynamically adjusting hyperparameters based on detected topology information. The system modifies hyperparameter values according to the specific hardware topology and model characteristics, enabling adaptive optimization that responds to varying computational environments and model requirements.
2Ease of operation
If hyperparameter tuning is performed exclusively during the training phase, then the inference phase is simplified, but model performance is limited due to topology indifference
Solution Approach 1:
The patent applies preliminary action by performing hyperparameter tuning during the training phase based on detected topology information, so that optimized hyperparameters are established before inference begins. This preliminary optimization ensures that the model is properly configured for its specific topology, improving performance without complicating the inference phase.
Solution Approach 2:
The system implements feedback by using detected topology information to inform hyperparameter selection during training, creating a closed-loop optimization process. The topology detection results feed into the hyperparameter tuning mechanism, allowing the system to adaptively configure models based on their actual runtime environment before deployment.
3Adaptability or versatility
If conventional topology-indifferent tuning is used, then system compatibility is improved, but inference efficiency is reduced
Solution Approach 1:
The patent applies dynamics by transitioning from static, topology-indifferent hyperparameter settings to dynamic, topology-aware hyperparameter selection. The system detects runtime topology information and adapts hyperparameter configurations accordingly, enabling efficient execution across diverse hardware platforms while maintaining system compatibility through automated adaptation.
Data Source
AI summary
Methods, apparatus, and processor-readable storage media for automated topology-aware deep learning inference tuning are provided herein. An example computer-implemented method includes obtaining input information from one or more systems associated with a datacenter; detecting topological information associated with at least a portion of the systems by processing at least a portion of the input information, wherein the topological information is related to hardware topology; automatically selecting one or more of multiple hyperparameters of at least one deep learning model based on the detected topological information; determining a status of at least a portion of the detected topological information by processing, during an inference phase of the at least one deep learning model, the detected topological information and data from at least one systems-related database; and performing, in connection with at least a portion of the selected hyperparameters, one or more automated actions based on the determining.