Dynamic Hardware Configuration via ML and FSM Engine Selection
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Solution Overview
Problem
Existing resource and state management systems in electronic devices face inefficiencies due to the limitations of finite state machines in handling complex operating characteristics and the lack of determinism in machine learning decisions, leading to suboptimal power and performance management.
Innovation Solution
A processor that dynamically selects between executing a finite state machine engine and a machine learning engine based on operating characteristics to configure hardware resources, weighing decisions equally or giving precedence to one over the other depending on the context, to optimize power and performance efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a finite state machine is used to make real-time decisions for hardware configuration, then deterministic performance is achieved, but decision-making becomes excessively slow and inefficient when handling complex operating characteristics
Solution Approach 1:
The system dynamically selects between finite state machine and machine learning engines based on the complexity and type of operating characteristics. For simple, well-defined states, the finite state machine provides deterministic control. For complex, nuanced operating characteristics, the machine learning engine takes over to improve decision speed and accuracy, thus adapting the decision-making mechanism to the specific situation.
Solution Approach 2:
The decision-making system is segmented into two separate engines: a finite state machine engine for deterministic, rule-based decisions and a machine learning engine for complex, data-driven decisions. This segmentation allows each engine to operate in its optimal domain, with the finite state machine handling simple states and the machine learning engine handling complex operating characteristics, thereby resolving the contradiction between reliability and productivity.
2Productivity
If machine learning is used to make real-time decisions for hardware configuration, then decision speed improves, but determinism is reduced leading to worse performance
Solution Approach 1:
An engine selection mechanism acts as an intermediary between the machine learning engine and the hardware configuration system. This intermediary evaluates the operating characteristics and determines whether to use machine learning decisions or finite state machine decisions. By introducing this intermediary, the system can leverage the speed of machine learning while maintaining the reliability of finite state machines when needed, thus resolving the contradiction between productivity and reliability.
Solution Approach 2:
Different decision-making approaches are applied to different operating characteristics based on their specific requirements. Machine learning is applied locally to complex, data-intensive operating characteristics where speed and adaptability are crucial. Finite state machine is applied locally to simple, well-defined states where determinism is paramount. This localized application of different methodologies resolves the contradiction by matching the right tool to the right task.
3Adaptability or versatility
If the system handles increasingly complex device operations with more operating characteristics, then adaptability improves, but the finite state machine becomes inefficient and takes excessively long to make decisions
Solution Approach 1:
The system employs a universal decision-making framework that can handle both simple and complex operating characteristics through two engines. The finite state machine engine handles well-defined, simple states, while the machine learning engine handles complex, nuanced operating characteristics. This multi-functional approach allows the system to maintain high adaptability across diverse scenarios while preserving decision-making efficiency by routing appropriate tasks to the suitable engine.
Data Source
AI summary
Systems and methods for configuring hardware resources of an electronic device based on operating characteristics of the electronic device are provided. In one embodiment, a processor may configure the hardware resources of the electronic device that equally weighs decisions made by a machine learning (ML) engine and a finite state machine (FSM) engine. In another embodiment, the processor may configure the hardware resources of the electronic device that gives greater weight to decisions made by the ML engine compared to decisions made by the FSM engine. In yet another embodiment, the processor may configure the hardware resources of the electronic device that gives greater weight to decisions made by the FSM engine compared to decisions made by the ML engine.