Dynamic Energy, Performance, and Accuracy Optimization and Management Using Automatically Generated Constraints for Separable 2D FIR Filtering for Digital Video Processing

Abstract

There is strong interest in the development of dynamically reconfigurable systems that can meet real-time constraints on energy, performance, and accuracy. The generation of real-time constraints will significantly expand the applicability of dynamically reconfigurable systems to new domains, such as digital video processing. We develop a dynamically reconfigurable 2D FIR filtering system that can meet real-time constraints in energy, performance, and accuracy (EPA). The real-time constraints are automatically generated based on user input, image types associated with video communications, and video content. We first generate a set of Pareto-optimal realizations, described by their EPA values and associated 2D FIR hardware description bitstreams. Dynamic management is then achieved by selecting Pareto-optimal realizations that meet the automatically generated time-varying EPA constraints. We validate our approach using three different 2D Gaussian filters. Filter realizations are evaluated in terms of the required energy per frame, accuracy of the resulting image, and performance in frames per second. We demonstrate dynamic EPA management by applying a Difference of Gaussians (DOG) filter to standard video sequences. For video frame sizes that are equal to or larger than the VGA resolution, compared to a static implementation, our dynamic system provides significant reduction in the total energy consumption (>30%).