Dynamic bandwidth adaptation using recognition accuracy prediction through pre-classification for embedded vision systems

Empowered by the massive growth of camera enabled mobile devices; mobile applications that allow users to perceive and experience the world in richer and more engaging ways have emerged at tremendous pace. As more complex perception algorithms are developed to take advantage of higher resolution imagery, future mobile applications will require application specific accelerators to maintain performance required for interactive user experiences. A key challenge in these accelerator-rich mobile platforms will be guaranteeing the off-chip memory bandwidth required by each accelerator. Device integration techniques such as Package on Package and Wide-IO seek to tackle the memory wall problem by reducing bottlenecks at the I/O interfaces. However, less effort has been focused on solving the bandwidth problem by dynamically leveraging the individual and collective bandwidth characteristics of accelerators operating concurrently. This work investigates the off-chip bandwidth characteristics of accelerators in the context of embedded perceptual computing applications. A bandwidth aware feedback system is proposed that dynamically partitions available bandwidth among a set of accelerators at the expense of application accuracy. As a case study, the proposed adaption policy is applied to a biologically-inspired scene understanding application. Results indicate that the system maintains good accuracy while requiring only 25% of the original bandwidth.