Energy-Aware Processing System for Adaptive ML Inference
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Solution Overview
Problem
Machine learning (ML) algorithms face challenges in energy efficiency, particularly in low-end devices with fluctuating power availability, consumer devices with battery limitations, and high-end applications seeking energy conservation, due to their rigidity and inability to adapt dynamically to varying resource availability, leading to inefficient energy use and potential data staleness.
Innovation Solution
An energy-aware processing system that degrades data signals using a source coding module based on available energy, generating outputs from an inference module with parameters dependent on energy measures, allowing for adaptive inference quality and reduced power consumption, while training the modules together to learn distortion-tolerant functions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If ML algorithms process high-fidelity data signals, then inference accuracy is improved, but energy consumption increases
Solution Approach 1:
The system dynamically adapts the processing pipeline to available energy levels by adjusting the degradation scalar and selecting appropriate inference modules. The source coding module continuously modifies the fidelity of degraded signals based on real-time energy measurements, enabling the system to transition smoothly between high-accuracy and low-energy modes without rigid thresholds.
Solution Approach 2:
The system changes key parameters including the degradation scalar applied to data signals, the fidelity level of processed signals, and the selection criteria for inference modules. These parameter adjustments are directly controlled by energy level measurements, allowing the system to optimize the balance between inference accuracy and energy consumption based on available power resources.
2Use of energy by moving object
If the system adapts inference quality to energy levels, then energy efficiency is improved, but system complexity increases
Solution Approach 1:
The system segments the inference functionality into multiple specialized modules, each optimized for specific energy levels and data fidelity requirements. This segmentation allows the system to select appropriate inference modules based on available energy, avoiding the need for a single complex adaptive module while achieving energy-efficient operation across different power conditions.
Solution Approach 2:
The source coding module serves multiple functions: it degrades data signals for energy conservation, controls the fidelity of input to inference modules, and adapts the overall processing pipeline to energy availability. This multi-functionality reduces system complexity by consolidating adaptation logic into a single versatile component rather than requiring separate control mechanisms for each inference module.
3Use of energy by moving object
If data signal fidelity is reduced, then power consumption is reduced, but inference quality deteriorates
Solution Approach 1:
The system applies different fidelity levels to different parts of the processing pipeline based on energy availability. Rather than uniformly reducing quality throughout, the source coding module selectively degrades specific aspects of the data signal while preserving critical features, and the inference module selection ensures that each processing stage receives appropriately fidelity-matched input.
4Device complexity
If the system uses fixed inference modules, then device complexity is reduced, but adaptability to energy fluctuations is limited
Solution Approach 1:
The system implements dynamic module selection where the choice of inference module is continuously adjusted based on measured energy levels. This dynamic behavior enables the system to adapt to fluctuating power availability while maintaining relatively simple fixed module designs, as the adaptation occurs at the selection level rather than requiring complex internal reconfiguration of each module.
Data Source
AI summary
An apparatus, method and computer program is described comprising: degrading an acquired data signal, using a source coding module, to generate a degraded signal having a fidelity dependent on a first measure of available energy, wherein the acquired data signal is degraded based on a scalar dependent on said first measure of available energy; and generating an output based on the degraded data signal, wherein the output is generated using an inference module that has parameters dependent on a second measure of available energy, wherein the inference module is configured to output degradable inferences dependent on the degraded signal received by the inference module from the source coding module.