Processor Power Control via Network Traffic Metadata
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Solution Overview
Problem
Client computing systems face challenges in managing power consumption and performance effectively due to varying network traffic characteristics, which affect Quality of Service (QoS) and user experience, as existing technologies do not adequately utilize network traffic metadata to optimize power management policies in real-time.
Innovation Solution
Hardware circuitry analyzes network traffic metadata using machine learning/AI to provide insights to microcontrollers and schedulers, enabling dynamic power management decisions that differentiate between real-time and non-real-time workloads, optimizing power and performance by allocating resources accordingly and adjusting core frequencies and memory hierarchies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If network traffic metadata is used to dynamically control processor operation, then power consumption is improved and battery life is extended, but device complexity increases due to additional hardware circuitry and machine learning classifiers
Solution Approach 1:
The system segments network traffic into different categories (real-time vs. non-real-time) using classifiers that analyze traffic metadata. This segmentation allows the processor to apply different power management policies to different traffic types, optimizing power consumption while maintaining necessary functionality. The classifier divides the network traffic handling into distinct categories that can be managed differently.
Solution Approach 2:
A hardware feedback circuit acts as an intermediary between the network interface and the processor cores. This intermediary receives traffic metadata from the network interface, processes it through classifiers, and generates feedback information that the scheduler uses to make informed decisions about thread placement and core frequency adjustment, thereby reducing power consumption without requiring direct complex control logic in every component.
2Ease of operation
If real-time traffic classification is implemented using machine learning classifiers, then user experience is improved through optimized responsiveness, but processing time is increased due to metadata analysis
Solution Approach 1:
The system performs preliminary classification of network traffic metadata at the network interface before traffic reaches the processor cores. By using classifiers to pre-analyze traffic characteristics and categorize them as real-time or non-real-time, the system prepares feedback information in advance, allowing the scheduler to make rapid decisions without performing complex analysis during critical processing moments, thus minimizing processing time overhead.
Solution Approach 2:
The patent replaces complex software-based traffic analysis with hardware-based classifiers and feedback circuits that operate at lower levels in the system architecture. This substitution moves the classification function from the software processing layer to the hardware network interface layer, enabling faster classification with minimal processing time impact on the main processor.
3Productivity
If processor cores are dynamically controlled based on traffic type, then productivity is improved through optimized performance allocation, but device complexity increases due to scheduler modifications
Solution Approach 1:
The system implements a feedback mechanism where traffic metadata is continuously analyzed by classifiers, and the resulting feedback information is provided to the scheduler in real-time. This feedback loop enables the scheduler to dynamically adjust thread placement and core frequency based on current traffic conditions, optimizing productivity. The feedback approach allows complex scheduling decisions to be made based on simple, continuously updated traffic category information.
Solution Approach 2:
The scheduler is designed with multi-functionality to handle both real-time and non-real-time traffic categories using the same basic scheduling infrastructure. By making the scheduler universal in its ability to respond to different traffic types through a unified feedback mechanism, the system avoids creating separate complex scheduling systems for different traffic types, thereby managing complexity while maintaining productivity optimization.
Data Source
AI summary
In one embodiment, a processor includes: a plurality of cores to execute instructions; a power controller to control power consumption of the plurality of cores, the power controller to receive network traffic metadata from a classifier and control the power consumption of at least one of the plurality of cores based at least in part on the network traffic metadata; and a hardware feedback circuit coupled to the plurality of cores, the hardware feedback circuit to determine hardware feedback information comprising an energy efficiency capability and a performance capability of at least some of the plurality of cores based at least in part on the network traffic metadata. Other embodiments are described and claimed.