  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 the sensor temperature, impairing the
fidelity of imaging/vision tasks. 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. We present parameters that govern the policy decisions and
explore the trade-offs between system power and policy overhead. Our evaluation
shows that our novel dynamic thermal management strategies can unlock the
energy-efficiency potential of near-sensor processing. For our evaluated tasks,
our strategies save up to 53% of system power with negligible performance
impact and sustained image fidelity.
