A case for temperature-driven task migration to balance energy efficiency and image quality of vision processing workloads

Many researchers in academia and industry [4, 8] advocate shifting processing near the image sensor through near-sensor accelerators to reduce data movement across energy-expensive interfaces. However, near-sensor processing also heats the sensor, increasing thermal noise and hot pixels, which degrades image quality. To understand these implications, we perform an energy and thermal characterization in the context of an augmented reality case study around visual marker detection. Our characterization results show that for a near-sensor accelerator consuming 1 W of power, dynamic range drops by 16 dB, image noise increases by 3 times, and the number of hot pixels multiplies by 16, degrading image quality. Such degradation impairs the task accuracy of interactive perceptual applications that require high accuracy. The marker-detection fails for 12% of frames when degraded by 1 minute of 1 W near-sensor power consumption. To this end, we propose temperature-driven task migration, a system-level technique that partitions processing between the thermally-coupled near-sensor accelerator and the thermally-isolated CPU host. Leveraging the sensor’s current temperature and application driven image fidelity requirements, this technique mitigates task accuracy issues while providing gains in energy-efficiency. We discuss challenges pertaining to effective, seamless migration decisions at runtime, and propose potential solutions.