Dynamic temperature management of near-sensor processing for energy-efficient high-fidelity imaging

Venkatesh Kodukula, Saad Katrawala, Britton Jones, Carole Jean Wu, Robert Likamwa

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


Vision processing on traditional architectures is inefficient due to energy-expensive off-chip data movement. Many researchers advocate pushing processing close to the sensor to substantially reduce data movement. However, continuous near-sensor processing raises sensor temperature, impairing imaging/vision fidelity. We characterize the thermal implications of using 3D stacked image sensors with near-sensor vision processing units. Our 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, our 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 our characterization, we propose and investigate two thermal management strategies—stop-capture-go and seasonal migration—for imaging-aware thermal management. For our evaluated tasks, our policies save up to 53% of system power with negligible performance impact and sustained image fidelity.

Original languageEnglish (US)
Article number926
Pages (from-to)1-20
Number of pages20
JournalSensors (Switzerland)
Issue number3
StatePublished - Feb 1 2021


  • Continuous mobile vision
  • Fidelity
  • Image sensors
  • Thermal management

ASJC Scopus subject areas

  • Analytical Chemistry
  • Biochemistry
  • Atomic and Molecular Physics, and Optics
  • Instrumentation
  • Electrical and Electronic Engineering


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