Runtime Thermal Management for Near-Sensor Vision Systems
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Solution Overview
Problem
Near-sensor processing in imaging systems reduces energy consumption but increases sensor temperature, degrading image quality due to thermal noise, and existing dynamic thermal management techniques fail to adequately address transient imaging needs.
Innovation Solution
Implementing a runtime controller that dynamically manages the operational mode of a vision system by applying thermal management policies such as stop-capture-go and seasonal migration, which adapt to image fidelity demands and ambient conditions, using clock gating and task offloading to regulate sensor temperature.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If near-sensor processing is implemented to reduce energy consumption, then energy efficiency is improved, but sensor temperature increases causing image quality degradation
Solution Approach 1:
The system implements periodic action by dynamically switching between near-sensor processing mode and far-sensor processing mode based on thermal conditions and image quality requirements. The runtime controller periodically evaluates temperature sensors and fidelity constraints to determine when to activate near-sensor processing and when to migrate tasks away, creating a rhythmic pattern of operation that balances energy efficiency with thermal management
Solution Approach 2:
The system applies dynamics by making the processing architecture adaptable and reconfigurable in real-time. The runtime controller dynamically adjusts the operational mode (near-sensor vs. far-sensor processing) based on changing thermal conditions, image fidelity requirements, and task priorities. This dynamic reconfiguration allows the system to optimize energy consumption when thermal conditions permit while maintaining image quality when temperatures rise
2Productivity
If continuous near-sensor processing is used to maintain high-speed capture, then productivity is improved, but temperature regulation fails causing fidelity loss
Solution Approach 1:
The system implements feedback mechanisms through temperature sensors that continuously monitor the thermal state of the sensor element and provide real-time information to the runtime controller. The controller uses this feedback to dynamically adjust processing mode, switching from near-sensor to far-sensor processing when temperature thresholds are approached, thereby preventing fidelity loss while maintaining high-speed capture capability when conditions allow
Solution Approach 2:
The runtime controller acts as an intermediary between the near-sensor processing unit and the far-sensor processing unit, mediating task allocation based on thermal conditions and fidelity requirements. It receives temperature feedback and fidelity constraints, then decides whether to keep tasks in the energy-efficient near-sensor mode or migrate them to the thermally isolated far-sensor mode, ensuring continuous high-speed capture while protecting image fidelity
3Loss of energy
If traditional dynamic thermal management is applied to reduce package cooling costs, then energy efficiency is improved, but transient imaging needs are not met causing performance loss
Solution Approach 1:
The system applies local quality by implementing fine-grained thermal management at the task level rather than applying blanket thermal management to the entire package. The runtime controller evaluates individual tasks and their thermal impact, selectively migrating only those tasks that would compromise image fidelity while keeping other tasks in near-sensor mode. This localized approach reduces unnecessary task migrations and maintains imaging performance while still managing thermal conditions effectively
Data Source
AI summary
Fidelity-driven runtime thermal management for near-sensor architectures is provided. In this regard, a runtime controller is provided for controlling an operational mode of a vision or imaging system driven by fidelity demands. The runtime controller is responsible for guaranteeing the fidelity demands of a vision application and coordinating state transfer between operating modes to ensure a smooth transition. Under this approach, the vision application only needs to provide the runtime controller with high-level vision/imaging fidelity demands and when to trigger them. The runtime controller translates these demands into effective thermal management. To do this, the runtime controller applies application-specific requirements into appropriate policy parameters and activates temperature reduction mechanisms, such as clock gating and task offload. Furthermore, the runtime controller continuously adapts the policy parameters to situational settings, such as ambient temperature and ambient lighting, to meet ongoing fidelity demands.