Platform Idle State Selection via Aggregated Device Latency

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Solution Overview

Problem

Conventional mobile computing platforms face limitations in idle state depth due to quality of service (QoS) requirements, which can negatively impact energy efficiency and performance.

Innovation Solution

The solution involves aggregating idle duration information from various devices to determine the deepest idle state that meets the platform's latency tolerance requirements, using an aggregator architecture to classify and manage deterministic, estimated, and statistical idle durations, and implementing pre-wake activities to optimize idle state transitions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Use of energy by moving object

If idle state depth is increased to improve energy efficiency, then power consumption is reduced, but quality of service requirements cannot be guaranteed

Engineering Contradiction:

Improvepower consumptionVSAvoidquality of service

Core Design Contradiction:

Use of energy by moving objectVSReliability

Solution Approach 1:

The system performs preliminary actions by aggregating idle duration information from multiple devices before selecting an idle state. This allows the platform to predict future idle periods and select deeper idle states that will satisfy QoS requirements, rather than being constrained by immediate latency needs. The pre-wake configuration enables the system to prepare for future wake events, allowing deeper idle states to be safely selected.

Inventive Principle:

Principle #10Preliminary action

2Reliability

If idle state depth is limited to guarantee quality of service, then service reliability is maintained, but energy efficiency deteriorates

Engineering Contradiction:

Improvequality of serviceVSAvoidpower consumption

Core Design Contradiction:

ReliabilityVSUse of energy by moving object

Solution Approach 1:

The system dynamically adjusts idle state selection based on aggregated idle duration information from multiple devices. Rather than using a fixed idle state depth, the platform adapts its idle state choice based on real-time conditions, selecting the deepest possible idle state that will still satisfy QoS requirements. This dynamic approach allows the system to optimize energy efficiency while maintaining service reliability.

Inventive Principle:

Principle #15Dynamics

3Use of energy by moving object

If deeper idle states are selected to improve energy efficiency, then power savings increase, but latency tolerance requirements become harder to meet

Engineering Contradiction:

Improvepower consumptionVSAvoidlatency

Core Design Contradiction:

Use of energy by moving objectVSLoss of time

Solution Approach 1:

The system incorporates feedback mechanisms by aggregating idle duration information from multiple devices and using this feedback to adjust idle state selection. The pre-wake configuration provides feedback about upcoming wake events, allowing the system to select idle states that balance energy savings with latency requirements. This feedback loop enables optimal idle state selection that considers both power consumption and latency tolerance.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS9015510B2Optimizing energy efficiency using device idle duration information and latency tolerance based on a pre-wake configuration of a platform associated to the device

Publication Date: 2015.04.21 TAHOE RES LTD

AI summary

Systems and methods may provide for aggregating a first idle duration from a first device associated with a platform and a second idle duration from a second device associated with the platform. Additionally, an idle state may be selected for the platform based at least in part on the first idle duration and the second idle duration. In one example, the idle durations are classified as deterministic, estimated or statistical.