ML Model Peer Ranking and Hardware Recommendations for Energy Efficiency
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Solution Overview
Problem
Existing technologies face challenges in fairly comparing different ML models and hardware on energy efficiency, difficulty in detecting excessive energy consumption, and lack of data for improving energy efficiency in machine learning workloads.
Innovation Solution
A framework that estimates energy efficiency based on ML model characteristics and hardware information, uses similarity measures to find peer models, provides a rank of similar models, stores data in a database for future comparisons, and suggests hardware modifications to enhance energy efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If different ML models and hardware are compared using traditional metrics, then model performance can be evaluated, but energy efficiency comparison becomes unfair and inaccurate
Solution Approach 1:
The patent introduces an intermediary system comprising a database and processing logic that standardizes energy efficiency measurements across different ML models and hardware configurations. This intermediary layer normalizes data collection, storage, and comparison, enabling fair energy efficiency assessments without requiring direct complex interactions between diverse models and hardware platforms.
Solution Approach 2:
The patent transforms energy efficiency measurements by changing the parameters used for comparison - moving from raw power consumption to normalized energy efficiency metrics that account for model characteristics and hardware specifications. This parameter transformation enables meaningful comparisons across heterogeneous systems by adjusting for relevant variables.
2Reliability
If energy consumption monitoring is implemented for ML workloads, then excessive energy consumption can be detected, but the system requires additional complexity and resources
Solution Approach 1:
The monitoring system is designed to leverage existing infrastructure and automatically collect energy consumption data from ML workloads without requiring external intervention. The system self-configures measurement protocols, automatically processes data through the intermediary layer, and generates detections based on predefined thresholds, reducing operational complexity while maintaining reliable detection capability.
3Productivity
If comprehensive data collection is performed to support ML and hardware recommendations, then energy efficiency improvements can be optimized, but data storage and processing requirements increase
Solution Approach 1:
The system extracts only the essential and most relevant features from comprehensive ML model and hardware data for storage and processing. Rather than retaining all raw data, the intermediary layer identifies and extracts key parameters related to energy efficiency, model characteristics, and hardware specifications, reducing data volume while maintaining recommendation accuracy.
Solution Approach 2:
The patent segments the comprehensive data collection into distinct categories and layers - model metadata, hardware specifications, energy consumption metrics, and performance measurements. This segmentation allows selective storage and processing of different data types based on their relevance to specific recommendation tasks, optimizing the balance between data completeness and resource requirements.
Data Source
AI summary
One example method includes obtaining input including characteristics of a machine learning (ML) model, specifications of a hardware configuration on which the ML model has been run, and characteristics of a prospective workload, estimating, given the hardware configuration, an energy efficiency of the ML model, using a similarity measure to find peer ML models of the ML model, and each peer ML model is more energy efficient, given the hardware configuration, than the ML model, ranking the peer ML models as suggested alternatives to the ML model, storing, in a database, the characteristics of the ML model, the energy efficiency of the ML model, and the specifications of the hardware configuration, and transmitting the specifications of the hardware configuration, along with workload characteristics, to a recipient.