Machine Learning Model Optimization via Latency-Aware Training
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Solution Overview
Problem
Machine learning model processing is resource-intensive, leading to increased carbon emissions and latency issues, which affect user experience and efficiency in providing predictions and results.
Innovation Solution
A method that involves temporarily deploying machine learning models during training, evaluating latency using a subset of training data, and optimizing the model based on latency values to reduce processing time and energy consumption, by adjusting features, quantization, or processing layers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If machine learning models are deployed to provide user interactions and predictions, then user experience and service functionality are improved, but resource consumption and carbon emissions increase significantly
Solution Approach 1:
The patent applies parameter changes by adjusting model configuration parameters (such as model size, complexity, and architecture) to optimize the balance between user experience and resource consumption. The system dynamically selects and configures machine learning models based on operational context, device capabilities, and performance requirements, thereby reducing unnecessary resource consumption while maintaining acceptable user experience.
Solution Approach 2:
The patent implements segmentation by dividing machine learning workloads into different components and deploying them strategically. This includes splitting models into smaller segments that can be executed on-device versus those requiring cloud processing, and dividing training and inference operations to reduce overall resource consumption and carbon footprint.
2Speed
If machine learning models are deployed to provide real-time predictions, then responsiveness to user interactions is improved, but processing time and computational resources increase
Solution Approach 1:
The patent applies preliminary action by pre-processing and pre-computing features during offline training phases, and by pre-loading or caching model artifacts before deployment. This reduces the computational burden during real-time inference, thereby improving responsiveness while minimizing actual processing time during user interactions.
Solution Approach 2:
The patent extracts and separates computationally intensive operations from real-time inference paths. By taking out heavy computational tasks and performing them offline or in batch processes, the system reduces the processing time required for real-time predictions while maintaining responsiveness to user interactions.
3Measurement precision
If machine learning models are trained with high accuracy requirements, then model performance is improved, but training resources and computational power increase
Solution Approach 1:
The patent applies partial action by implementing progressive training strategies where models are trained to sufficient rather than maximum accuracy levels. The system uses techniques such as early stopping, progressive refinement, and selective training on critical subsets of data, achieving acceptable accuracy with reduced computational power and training resources.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for optimizing a machine learning model. In some implementations, a method includes performing, during the model training process, model training for the machine learning model using training data; in response to performing the model training, generating a temporary deployment of the machine learning model; providing, as input to the temporarily deployed machine learning model, a portion of the training data including one or more elements; obtaining, based on processing of the portion of the training data by the temporarily deployed machine learning model, response data indicating output of the temporarily deployed machine learning model; determining a latency value indicating a processing time for the temporarily deployed machine learning model to generate the response data; and optimizing the machine learning model using the latency value.