Neural-Network Cooling Control for Server and SSD Workload Heat
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Solution Overview
Problem
Traditional cooling methods for computing devices, such as servers and SSDs, are static and do not adapt to changing conditions, leading to inefficient thermal management and increased power usage as they rely on pre-determined settings that do not account for dynamic workloads and temperature fluctuations.
Innovation Solution
An artificial intelligence system, utilizing neural networks, is integrated into the cooling management system to dynamically determine thermal settings based on real-time temperature and workload data, allowing for predictive and adaptive cooling adjustments to maintain optimal performance and reduce energy consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If static cooling settings are used, then device complexity is reduced, but thermal management efficiency deteriorates
Solution Approach 1:
The patent implements dynamic cooling control by transitioning from static pre-determined settings to adaptive thermal management. The system continuously monitors temperature sensors and adjusts cooling parameters in real-time based on actual thermal conditions and workload characteristics, enabling the cooling system to dynamically respond to changing operational states
Solution Approach 2:
The patent incorporates feedback mechanisms through temperature sensors that continuously monitor thermal conditions and feed this information back to the cooling control system. This closed-loop feedback enables the system to detect temperature deviations and automatically adjust cooling parameters to maintain optimal thermal conditions
2Ease of operation
If pre-determined cooling settings are used, then ease of operation is improved, but adaptability to changing conditions deteriorates
Solution Approach 1:
The patent enables the cooling system to self-adjust by automatically monitoring temperature conditions and workload parameters, then autonomously modifying cooling settings without requiring manual intervention. The system learns from operational patterns and independently optimizes thermal management based on real-time conditions
Solution Approach 2:
The system transitions from fixed static settings to dynamic adaptive control, where cooling parameters automatically adjust in response to changing workload conditions and thermal states, enabling the system to adapt to varying operational requirements
3Device complexity
If static thermal settings are used, then device complexity is reduced, but energy consumption increases
Solution Approach 1:
The patent applies partial cooling action by adjusting cooling intensity to match actual thermal requirements rather than applying maximum or constant cooling. The system modulates cooling output based on real-time temperature and workload conditions, applying only the necessary cooling effort needed to maintain thermal conditions
Solution Approach 2:
The system dynamically adjusts cooling power consumption by transitioning from static full-capacity cooling to adaptive variable cooling, where power usage is optimized based on actual thermal conditions and workload demands
4Reliability
If AI-based dynamic cooling control is implemented, then thermal management efficiency is improved, but device complexity increases
Solution Approach 1:
The patent introduces an AI-based intermediary layer that sits between temperature sensors and cooling actuators. This intelligent intermediary processes thermal data, predicts future thermal states, and determines optimal cooling actions, bridging the gap between simple sensing and complex cooling control
5Temperature
If AI-based predictive cooling is implemented, then peak temperature reduction is achieved, but computational overhead increases
Solution Approach 1:
The patent implements preliminary cooling action by using AI to predict future thermal states and proactively adjusting cooling parameters before peak temperatures occur. The system anticipates thermal increases based on workload patterns and pre-adjusts cooling to prevent temperature spikes
Data Source
AI summary
According to one general aspect, an apparatus may include a memory storage device. The memory storage device may include a plurality of memory cells configured to store data. The memory storage device may include a first temperature sensor configured to detect a temperature of the memory cells. The memory storage device may include an artificial intelligence system configured to dynamically determine a thermal management setting to be employed by the memory storage device, based, at least in part, upon the first temperature sensor and a workload of the memory storage device. The memory storage device may dynamically changes a set of operational parameters in response to the thermal management setting determined by the artificial intelligence system.