Machine Learning Training Device With Energy-Saving Clustering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing machine learning training processes are resource-intensive and often involve redundant training of similar models, leading to significant energy waste and increased carbon footprint, with no existing devices addressing this inefficiency.
Innovation Solution
A machine learning training device that includes a queuing system, clustering mechanism, and energy estimator to consolidate similar training requests into a consolidated training block, calculating energy savings and sharing model weights across these requests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If machine learning training requests are processed separately, then each request can be trained independently with dedicated resources, but energy consumption increases and redundant training occurs
Solution Approach 1:
The patent consolidates multiple similar machine learning training requests into a single consolidated training block, merging redundant training operations into one execution. The clustering device groups training requests by similarity, and the training engine executes them together, sharing computational resources and model weights, thereby significantly reducing energy consumption while eliminating wasteful redundant training.
Solution Approach 2:
The consolidated training block serves multiple training requests simultaneously, making a single training operation universal for several purposes. By sharing model weights and computational processes across multiple requests, the system achieves multi-functionality where one training execution benefits multiple users or applications, reducing overall energy consumption.
2Loss of energy
If similar training requests are consolidated into a consolidated training block, then energy consumption decreases, but the device complexity increases due to clustering and comparison processes
Solution Approach 1:
The system performs preliminary clustering and similarity comparison of training requests before actual training execution. By pre-grouping similar requests into consolidated training blocks and identifying shared model weights in advance, the system prepares the training workload optimally, ensuring energy efficiency is achieved without adding complexity during the actual training execution phase.
Solution Approach 2:
The energy estimator provides feedback on the energy consumption characteristics of training requests, enabling the clustering device to make informed decisions about consolidation. This feedback mechanism allows the system to continuously optimize the clustering strategy based on actual energy usage patterns, balancing the complexity of the clustering mechanism with the energy savings achieved.
3Reliability
If redundant training of similar models is performed, then each training request receives dedicated attention, but carbon footprint increases due to wasted energy
Solution Approach 1:
The patent merges redundant training operations into a single consolidated execution, eliminating the harmful effect of wasted energy and carbon footprint. By identifying similar training requests and consolidating them, the system maintains training quality through shared model weights while dramatically reducing the carbon footprint associated with redundant computational operations.
4Ease of operation
If no consolidation mechanism is implemented, then training requests are processed straightforwardly, but energy inefficiency and wasted resources increase
Solution Approach 1:
The system implements self-service through automated clustering and consolidation mechanisms that operate without manual intervention. The clustering device automatically groups similar training requests, the energy estimator evaluates consolidation opportunities, and the training engine executes consolidated blocks, maintaining ease of operation while dramatically improving energy efficiency through intelligent automation.
Data Source
AI summary
A machine learning training device having a clustering device configured to group machine learning training requests into a consolidated training block. The clustering device includes an isomorphism engine and an energy estimator configured to construct the consolidated training block based upon an amount of energy that will be saved by training the machine learning training requests together, thereby reducing an overall energy consumption by machine learning processes.