INTEGRATED INPUT MODELING AND MEMORY MANAGEMENT FOR IMAGE PROCESSING APPLICATIONS

Abstract

Image processing applications often demand powerful calculations and real-time performance with low power and energy consumption. Programmable hardware provides inherent parallelism and flexibility making it a good implementation choice for this application domain. In this work we introduce a new modeling technique combining Cyclo-Static Dataflow (CSDF) base model semantics and Homogeneous Parameterized Dataflow (HPDF) meta-modeling framework, which exposes more levels of parallelism than previous models and can be used to reduce buffer sizes. We model two different applications and show how we can achieve efficient scheduling and memory organization, which is crucial for this application domain, since large amounts of data are processed, and storing intermediate results usually requires the use of off-chip resources, causing slower data access and higher power consumption. We also designed a reusable wishbone compliant memory controller module that can be used to access the Xilinx Multimedia Board's memory chips using single accesses or burst mode.