Predictive Processor Component Suspension for Power Optimization
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Solution Overview
Problem
Pipelined processing devices experience significant power consumption and wasted cycles due to mispredicted branches, as they maintain components in an enabled state even when they are not required for instruction execution.
Innovation Solution
A suspend controller dynamically suspends processor and peripheral components by estimating instruction cycles and isolating unused components from power domains or using clock gating, reducing power consumption during predicted idle periods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If components are maintained in an enabled state to ensure immediate execution capability, then processing speed is improved, but power consumption increases
Solution Approach 1:
The system dynamically adjusts component states between enabled and suspended based on predicted instruction requirements. The suspend controller monitors branch prediction outcomes and instruction cache status to determine when to suspend or enable components, making the system adaptable to varying workload demands rather than maintaining a fixed enabled state
Solution Approach 2:
The system performs preliminary analysis of incoming instruction streams using branch prediction logic to anticipate which components will be needed. By predicting future instruction requirements before execution, the system can proactively suspend components that are unlikely to be needed, reducing power consumption while maintaining readiness for predicted workloads
2Use of energy by moving object
If components are suspended to reduce power consumption, then power usage is improved, but processing delay increases
Solution Approach 1:
The system uses branch prediction to perform preliminary analysis of incoming instruction streams, identifying which components will be needed before they are actually required. This allows components to remain enabled when needed and be suspended only when prediction indicates they will not be required, minimizing unnecessary delays
Solution Approach 2:
The system implements feedback mechanisms where the suspend controller continuously monitors branch prediction outcomes and instruction cache status. When mispredictions occur or instructions are loaded into the cache, the system adjusts component states in response, creating a feedback loop that optimizes the balance between power savings and processing delay
3Productivity
If branch prediction is used to preload instructions, then processing efficiency is improved, but power consumption increases due to mispredictions
Solution Approach 1:
The system monitors branch prediction accuracy and uses this feedback to adjust component suspension decisions. When mispredictions occur, the system learns from the outcome and modifies future prediction and suspension decisions, optimizing the balance between keeping components enabled for efficiency and suspending them to save power
Solution Approach 2:
The system dynamically adjusts its branch prediction aggressiveness and component suspension strategy based on observed prediction accuracy. Rather than using a fixed prediction threshold, the system adapts its behavior based on feedback from actual prediction outcomes, making the prediction mechanism itself dynamic and responsive to performance metrics
Data Source
AI summary
An instruction cycle is determined from instructions stored in a cache, where the instruction cycle represents the sequence of instructions predicted to be executed by the processing device that are resident in the cache. The duration of the instruction cycle is estimated and one or more components of the processing device that are not expected to be used during the instruction cycle may be suspended for a portion or all of the duration. The components may be suspended by, for example, clock gating or by isolating the components from one or more power domains.