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High spatiotemporal resolution can offer high precision for vision applications, which is particularly useful to capture the nuances of visual features, such as for augmented reality. Unfortunately, capturing and processing high spatiotemporal visual frames generates energy-expensive memory traffic. On the other hand, low resolution frames can reduce pixel memory throughput, but reduce also the opportunities of high-precision visual sensing. However, our intuition is that not all parts of the scene need to be captured at a uniform resolution. Selectively and opportunistically reducing resolution for different regions of image frames can yield high-precision visual computing at energy-efficient memory data rates. To this end, we develop a visual sensing pipeline architecture that flexibly allows application developers to dynamically adapt the spatial resolution and update rate of different “rhythmic pixel regions” in the scene. We develop a system that ingests pixel streams from commercial image sensors with their standard raster-scan pixel read-out patterns, but only encodes relevant pixels prior to storing them in the memory. We also present streaming hardware to decode the stored rhythmic pixel region stream into traditional frame-based representations to feed into standard computer vision algorithms. We integrate our encoding and decoding hardware modules into existing video pipelines. On top of this, we develop runtime support allowing developers to flexibly specify the region labels. Evaluating our system on a Xilinx FPGA platform over three vision workloads shows 43 − 64% reduction in interface traffic and memory footprint, while providing controllable task accuracy. 

Mobile vision systems would benefit from the ability to situationally sacrifice image resolution to save system energy when imaging detail is unnecessary. Unfortunately, any change in sensor resolution leads to a substantial pause in frame delivery -- as much as 280 ms. Frame delivery is bottlenecked by a sequence of reconfiguration procedures and memory management in current operating systems before it resumes at the new resolution. This latency from reconfiguration impedes the adoption of otherwise beneficial resolution-energy tradeoff mechanisms. We propose Banner as a media framework that provides a rapid sensor resolution reconfiguration service as a modification to common media frameworks, e.g., V4L2. Banner completely eliminates the frame-to-frame reconfiguration latency (226 ms to 33 ms), i.e., removing the frame drop during sensor resolution reconfiguration. Banner also halves the end-to-end resolution reconfiguration latency (226 ms to 105 ms). This enables a more than 49% reduction of system power consumption by allowing continuous vision applications to reconfigure the sensor resolution to 480p compared with downsampling from 1080p to 480p, as measured in a cloud-based offloading workload running on a Jetson TX2 board. As a result, Banner unlocks unprecedented capabilities for mobile vision applications to dynamically reconfigure sensor resolutions to balance the energy efficiency and task accuracy tradeoff.