Neural Link Power Control for Multi-Processor Data Transfer
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Solution Overview
Problem
Existing computing systems with multiple processors face inefficiencies due to excess power consumption when communication links operate at full power even during idle data transfer, and current bandwidth adjustments do not optimize data transfer rates for various processing tasks.
Innovation Solution
A hardware controller manages communication links by collecting performance metrics, using a neural network to predict optimal power states and frequencies, and implementing turbo-boosting or DVFS adjustments to maximize performance and minimize power consumption based on device activity and historical data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If communication links operate at full power to ensure data transfer capability, then data transfer reliability is improved, but power consumption increases
Solution Approach 1:
The system dynamically adjusts communication link power states based on real-time workload conditions. The hardware controller monitors device activity and transitions links between different power states (fully powered, partially powered, fully powered down) to match actual data transfer needs, resolving the contradiction between maintaining reliability and reducing power consumption.
Solution Approach 2:
The system changes operational parameters of communication links by adjusting power states and frequencies based on workload characteristics. The hardware controller modifies voltage, frequency, and power state parameters dynamically, allowing the system to achieve both reliable data transfer when needed and reduced power consumption when workload is low.
2Productivity
If bandwidth is adjusted by changing the number of links utilized, then data transfer capacity is improved, but control over data transfer rate is lost
Solution Approach 1:
The system dynamically adjusts both the number of active links and the data transfer rate on each link based on workload conditions. The hardware controller independently controls link activation and data transfer rate parameters, providing both high capacity when needed and precise control over transfer rates to optimize performance for different processing tasks.
3Speed
If communication links are fully powered to maximize data transfer rate, then processing speed is improved, but power consumption increases
Solution Approach 1:
The system dynamically adjusts communication link power states and frequencies based on real-time workload conditions. The hardware controller monitors device activity and transitions links between different power states (fully powered for maximum speed, partially powered for moderate speed, fully powered down for idle conditions), resolving the contradiction between maintaining high data transfer rates and reducing power consumption.
Solution Approach 2:
The system periodically monitors workload conditions and adjusts communication link power states accordingly. The hardware controller continuously evaluates data transfer requirements and transitions links between power states in periodic cycles, ensuring high performance when needed while minimizing power consumption during idle or low-demand periods.
Data Source
AI summary
Apparatuses, systems, and techniques to optimize device communications disclosed. In at least one embodiment, one or more neural networks are used to determine optimal power and frequency states for communication links between processing devices.