The design of a high-performance scalable architecture for image processing applications

Abstract

The authors present the organization of an interleaved wrap-around memory system for a partitionable parallel/pipeline architecture with P pipes of L processors each. The architecture is designed to efficiently support real-time image processing and computer vision algorithms, especially those requiring global data operations. The interleaved memory system makes the architecture highly scalable in that L and P can be chosen to optimize performance for particular problems and reconfigurable in that, once L and P are fixed, problems of any size can still be mapped onto the architecture. The authors demonstrate techniques and methods for mapping computational structures to the architecture by considering the case of the 1-D butterfly network (1DBN). Since many other computational structures can be mapped to 1DBN, this gives a firm application base for the architecture. The authors also demonstrate methods for scheduling and controlling the memory system.<>