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Stagioni: Temperature Management to ...
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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.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Stagioni: Temperature Management to Enable Near-Sensor Processing for Performance, Fidelity, and Energy-Efficiency of Vision and Imaging Workloads./
作者:
Kodukula, Venkatesh.
出版者:
Ann Arbor : ProQuest Dissertations & Theses, : 2019,
面頁冊數:
53 p.
附註:
Source: Masters Abstracts International, Volume: 80-06.
Contained By:
Masters Abstracts International80-06.
標題:
Computer Engineering. -
電子資源:
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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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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