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Stagioni: Temperature Management to ...

Kodukula, Venkatesh.

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Stagioni: Temperature Management to Enable Near-Sensor Processing for Performance, Fidelity, and Energy-Efficiency of Vision and Imaging Workloads.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Stagioni: Temperature Management to Enable Near-Sensor Processing for Performance, Fidelity, and Energy-Efficiency of Vision and Imaging Workloads./
Author:	Kodukula, Venkatesh.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
Description:	53 p.
Notes:	Source: Masters Abstracts International, Volume: 80-06.
Contained By:	Masters Abstracts International80-06.
Subject:	Computer Engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10973000
ISBN:	9780438712669

Stagioni: Temperature Management to Enable Near-Sensor Processing for Performance, Fidelity, and Energy-Efficiency of Vision and Imaging Workloads.
Kodukula, Venkatesh.

Stagioni: Temperature Management to Enable Near-Sensor Processing for Performance, Fidelity, and Energy-Efficiency of Vision and Imaging Workloads. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 53 p.

Source: Masters Abstracts International, Volume: 80-06.

Thesis (M.S.)--Arizona State University, 2019.

This item must not be sold to any third party vendors.

Vision processing on traditional architectures is inefficient due to energy-expensive off-chip data movements. Many researchers advocate pushing processing close to the sensor to substantially reduce data movements. However, continuous near-sensor processing raises the sensor temperature, impairing the fidelity of imaging/vision tasks. The work characterizes the thermal implications of using 3D stacked image sensors with near-sensor vision processing units. The characterization reveals that near-sensor processing reduces system power but degrades image quality. For reasonable image fidelity, the sensor temperature needs to stay below a threshold, situationally determined by application needs. Fortunately, the characterization also identifies opportunities - unique to the needs of near-sensor processing - to regulate temperature based on dynamic visual task requirements and rapidly increase capture quality on demand. Based on the characterization, the work proposes and investigate two thermal management strategies - stop-capture-go and seasonal migration - for imaging-aware thermal management. The work present parameters that govern the policy decisions and explore the trade-offs between system power and policy overhead. The work's evaluation shows that the novel dynamic thermal management strategies can unlock the energy-efficiency potential of near-sensor processing with minimal performance impact, without compromising image fidelity.

ISBN: 9780438712669Subjects--Topical Terms:

1567821
Computer Engineering.

Stagioni: Temperature Management to Enable Near-Sensor Processing for Performance, Fidelity, and Energy-Efficiency of Vision and Imaging Workloads.
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520 $a Vision processing on traditional architectures is inefficient due to energy-expensive off-chip data movements. Many researchers advocate pushing processing close to the sensor to substantially reduce data movements. However, continuous near-sensor processing raises the sensor temperature, impairing the fidelity of imaging/vision tasks. The work characterizes the thermal implications of using 3D stacked image sensors with near-sensor vision processing units. The characterization reveals that near-sensor processing reduces system power but degrades image quality. For reasonable image fidelity, the sensor temperature needs to stay below a threshold, situationally determined by application needs. Fortunately, the characterization also identifies opportunities - unique to the needs of near-sensor processing - to regulate temperature based on dynamic visual task requirements and rapidly increase capture quality on demand. Based on the characterization, the work proposes and investigate two thermal management strategies - stop-capture-go and seasonal migration - for imaging-aware thermal management. The work present parameters that govern the policy decisions and explore the trade-offs between system power and policy overhead. The work's evaluation shows that the novel dynamic thermal management strategies can unlock the energy-efficiency potential of near-sensor processing with minimal performance impact, without compromising image fidelity.
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