Predictive Temporal Attention on Event-based Video Stream for
  Energy-efficient Situation Awareness
Yiming Bu Jiayang Liu Qinru Qiu
The Dynamic Vision Sensor (DVS) is an innovative technology that efficiently
captures and encodes visual information in an event-driven manner. By combining
it with event-driven neuromorphic processing, the sparsity in DVS camera output
can result in high energy efficiency. However, similar to many embedded
systems, the off-chip communication between the camera and processor presents a
bottleneck in terms of power consumption. Inspired by the predictive coding
model and expectation suppression phenomenon found in human brain, we propose a
temporal attention mechanism to throttle the camera output and pay attention to
it only when the visual events cannot be well predicted. The predictive
attention not only reduces power consumption in the sensor-processor interface
but also effectively decreases the computational workload by filtering out
noisy events. We demonstrate that the predictive attention can reduce 46.7% of
data communication between the camera and the processor and reduce 43.8%
computation activities in the processor.