Guard Band Controller for Dynamic Hardware Resource Optimization
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Solution Overview
Problem
Computing devices face inefficiencies in power management due to conservative guard bands, leading to excessive energy consumption and performance losses, especially when handling computationally intensive tasks, as existing methods struggle to dynamically adapt to varying workloads and voltage droops.
Innovation Solution
A guard band controller that utilizes machine learning and artificial intelligence to dynamically adjust the safety margins of hardware resources based on workload phases, predicting optimal guard bands and implementing a fast-reactive voltage droop mitigation loop to maintain high reliability and reduce power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conservative guard bands are used to ensure system reliability, then voltage droop mitigation is improved, but power consumption increases and computing performance decreases
Solution Approach 1:
The patent applies dynamics by transitioning from static guard bands to dynamic guard bands that adapt in real-time based on workload characteristics. The system continuously monitors workload phase and adjusts guard band values dynamically, allowing the system to maintain reliability when needed while reducing power consumption during workloads that tolerate voltage droop, thereby resolving the contradiction between reliability and energy efficiency
Solution Approach 2:
The patent changes the parameter of guard band voltage from fixed conservative values to variable values based on workload analysis. By adjusting the guard band parameter dynamically according to workload phase and type, the system optimizes the balance between reliability (maintaining sufficient guard bands when necessary) and power consumption (reducing guard bands when workloads can tolerate voltage variations), thus resolving the technical contradiction
2Reliability
If conservative guard bands are used to ensure system reliability, then voltage droop mitigation is improved, but computing performance decreases
Solution Approach 1:
The system dynamically adjusts guard bands based on real-time workload monitoring, enabling high computing performance during workloads that are tolerant of voltage droop while maintaining system reliability during critical operations. This dynamic adaptation resolves the contradiction by making guard bands flexible rather than uniformly conservative
Solution Approach 2:
The patent changes the guard band parameter from fixed conservative values to workload-dependent variable values. By analyzing workload phase and adjusting the guard band parameter accordingly, the system achieves high computing performance when voltage droop is acceptable while maintaining reliability when needed, thus resolving the contradiction between reliability and productivity
3Use of energy by moving object
If dynamic guard band adjustment is implemented, then power efficiency is improved, but system complexity increases
Solution Approach 1:
The patent implements feedback mechanisms that monitor workload phase and system state, using this information to dynamically adjust guard bands. The feedback loop enables the system to optimize power efficiency through adaptive guard band adjustment while managing complexity through structured monitoring and control algorithms that analyze workload characteristics and translate them into appropriate guard band settings
4Reliability
If conservative operating setpoints are used, then system reliability is improved, but energy expenditure increases
Solution Approach 1:
The patent transitions from static conservative operating setpoints to dynamic setpoints that adapt based on workload phase. By continuously adjusting operating parameters according to real-time workload characteristics, the system maintains reliability during critical operations while reducing energy expenditure during workloads that can tolerate more aggressive settings, thus resolving the contradiction between reliability and energy loss
Data Source
AI summary
Methods, apparatus, systems and articles of manufacture are disclosed to optimize a guard band of a hardware resource. An example apparatus includes at least one storage device, and at least one processor to execute instructions to identify a phase of a workload based on an output from a machine-learning model, the phase based on a utilization of one or more hardware resources, and based on the phase, control a guard band of a first hardware resource of the one or more hardware resources.