Autonomous State Management Driver for Dynamic Power and Performance
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Solution Overview
Problem
Conventional computing devices operate in static state configurations, lacking adaptability and autonomy in managing power consumption, performance, and temperature, leading to inefficient use of resources and user discomfort due to fixed binary decision-making.
Innovation Solution
An autonomous system-state management (ASM) driver interfaces between the operating system and firmware to dynamically select between different modes of operation based on various monitored input parameters, prioritizing both power efficiency and performance while maintaining responsiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional devices operate in static state configurations, then device simplicity is maintained, but power efficiency and adaptability deteriorate
Solution Approach 1:
The patent implements dynamic state management by transitioning from static configuration to runtime-dynamic state selection. The system continuously monitors input parameters and dynamically selects optimal states from a predefined set, enabling adaptability without requiring complex real-time optimization algorithms. This dynamic approach resolves the contradiction by making the system adaptable while maintaining manageable complexity through state discretization.
Solution Approach 2:
The system changes operational parameters by selecting from multiple predefined states, each representing a different configuration of system parameters. Instead of continuously adjusting parameters (which would increase complexity), the system discretizes parameter space into distinct states and selects the optimal state based on monitored conditions. This approach provides adaptability through parameter variation while keeping the control mechanism simple.
2Productivity
If binary decision-making is used for state management, then decision simplicity is maintained, but power efficiency and performance optimization deteriorate
Solution Approach 1:
The patent replaces static binary decision-making with dynamic multi-state selection. The system monitors input parameters in real-time and dynamically transitions between multiple operational states (not just two), enabling performance optimization across a broader range of operating conditions. This dynamic multi-state approach improves productivity while maintaining simple decision logic through predefined state transitions.
Solution Approach 2:
The system segments the operational space into multiple discrete states rather than using a single binary decision. Each state represents a specific operational mode with optimized parameters for certain conditions. This segmentation allows the system to achieve better performance by selecting the appropriate segment (state) based on current conditions, while keeping each individual state simple and manageable.
3Use of energy by moving object
If multiple input parameters are monitored for state selection, then power efficiency and performance optimization improve, but measurement and detection difficulty increases
Solution Approach 1:
The patent segments the continuous parameter space into discrete ranges and thresholds for each monitored parameter. Instead of requiring precise continuous measurement and complex optimization algorithms, the system defines simple threshold-based segmentation for each input parameter. This approach enables power-efficient operation by allowing the system to determine optimal states through simple comparisons rather than complex calculations, while still responding to multiple input parameters.
Solution Approach 2:
The system changes from requiring precise continuous parameter measurement to using threshold-based parameter ranges. Each operational state is defined by specific ranges of input parameters rather than exact values. This parameter transformation simplifies detection and measurement requirements while maintaining the ability to optimize power consumption and performance based on multiple monitored parameters.
4Loss of energy
If dynamic state selection is implemented, then power efficiency and performance improve, but system complexity increases
Solution Approach 1:
The patent implements dynamic state selection with a focus on simplicity through predefined state transitions. Rather than implementing complex real-time optimization, the system uses a manageable set of predefined states with clear transition criteria. This dynamic approach improves power efficiency by selecting optimal states based on current conditions, while the complexity is kept in check through the use of discrete, pre-characterized states rather than continuous optimization.
Solution Approach 2:
The system uses a set of predefined, simple operational states that can be easily implemented and managed. Each state represents a simplified operational mode that is easy to implement and switch between. This approach achieves power efficiency through dynamic state selection while keeping the control system complexity low by using simple, discrete states rather than complex continuous control mechanisms.
Data Source
AI summary
A computing device is provided which comprises memory and a processor in communication with the memory. The processor is configured to autonomously acquire input parameter values, comprising one of monitored device input parameter values from a component of the computing device and monitored user input parameter values. The processor is also configured to select, from a plurality of modes of operation, a mode of operation comprising parameter settings which are determined based on the acquired input parameter values, each of the plurality of modes of operation comprising different parameter settings configured to control the computing device to operate at a different level of performance. The processor is also configured to control operation of the computing device by tuning the parameter settings of the computing device according to the selected mode of operation comprising the determined parameter settings.