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Dynamic Temperature Management of Near-Sensor Processing for Energy-Efficient High-Fidelity Imaging
Venkatesh Kodukula1, Saad Katrawala1, Britton Jones1
1School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ 85281, USA.
Sensors (Basel, Switzerland)
|February 12, 2021
Summary
Near-sensor vision processing reduces power but can overheat image sensors. New thermal management strategies balance power savings with essential image quality for efficient vision systems.
Area of Science:
- Computer Engineering
- Electrical Engineering
- Image Processing
Background:
- Traditional vision processing is power-intensive due to off-chip data movement.
- Near-sensor processing minimizes data transfer but introduces thermal challenges.
- Elevated sensor temperatures degrade image quality and vision system performance.
Purpose of the Study:
- To characterize the thermal effects of integrating 3D stacked image sensors with near-sensor vision processing units.
- To investigate thermal management strategies for maintaining image fidelity during continuous near-sensor processing.
- To optimize system power consumption without compromising vision performance.
Main Methods:
- Characterization of thermal implications in 3D stacked image sensors with integrated vision processing.
- Analysis of the trade-off between system power reduction and image quality degradation.
- Development and evaluation of two novel thermal management strategies: stop-capture-go and seasonal migration.
- Assessment of dynamic temperature regulation based on visual task requirements.
Main Results:
- Near-sensor processing effectively reduces system power but can lead to unacceptable sensor temperatures and reduced image quality.
- A critical temperature threshold, dependent on application needs, must be maintained for acceptable image fidelity.
- The proposed imaging-aware thermal management policies achieved up to 53% system power savings.
- Negligible performance impact and sustained image fidelity were observed with the implemented strategies.
Conclusions:
- Thermal management is crucial for enabling efficient near-sensor vision processing with 3D stacked image sensors.
- Dynamic, task-aware thermal regulation strategies can significantly reduce power consumption while preserving image quality.
- The proposed methods offer a viable solution for energy-efficient and high-fidelity vision systems in resource-constrained environments.
