Per-Lane Bus Power Management for Bursty Data Transfers
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Solution Overview
Problem
Conventional approaches to managing power states of bus lanes in communication systems are suboptimal for bursty traffic, as they do not account for future workload demands, leading to inefficiencies and performance penalties.
Innovation Solution
Implementing a future bus load characterization unit (FBLCU) to analyze workload operations, determine specific resource transfers, and adjust bus lane power states proactively based on future demand, including wake and sleep times for optimal performance and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by stationary object
If conventional per-lane power state control is used, then power consumption is reduced when demand is low, but performance deteriorates for bursty traffic because lanes are not powered up in time
Solution Approach 1:
The patent applies preliminary action by predicting future bus lane demand before it occurs and proactively adjusting power states in advance. The load predictor analyzes upcoming workload characteristics and triggers power state transitions before the actual demand hits, ensuring lanes are ready when needed while maximizing power savings during low-demand periods.
Solution Approach 2:
The system implements feedback through continuous monitoring of actual bus lane usage patterns and comparing them against predictions. This feedback loop allows the load predictor to refine its models and adjust power management decisions dynamically, optimizing both power consumption and performance based on real-world behavior.
2Loss of energy
If per-lane power management is implemented, then power efficiency improves, but system complexity increases due to additional control mechanisms
Solution Approach 1:
The load predictor serves multiple functions: it characterizes future bus lane demand, predicts workload patterns, and triggers power state transitions. By consolidating these functions into a single predictive component, the system achieves sophisticated power management without proportionally increasing complexity.
Solution Approach 2:
The system applies self-service by having the load predictor automatically adjust power states based on predicted demand without requiring manual intervention or complex external control logic. The predictive algorithm autonomously makes decisions about when to power lanes up or down, simplifying the overall control architecture.
Data Source
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AI summary
A method includes receiving a request for a transfer of data on a bus of a computing device; determining a direction for the transfer, at least in part based on the request; determining a quantity of data for the transfer, at least in part based on the request; determining a power state for a lane of the bus, at least in part based on the direction and the quantity of data for the transfer; and setting the power state for the lane of the bus.