Mapping Applications to the RaPiD Con gurable Architecture

The goal of the RaPiD (Recon gurable Pipelined Datapath) architecture is to provide high performance con gurable computing for a range of computationally-intensive applications that demand special-purpose hardware. This is accomplished by mapping the computation into a deep pipeline using a con gurable array of coarse-grained computational units. A key feature of RaPiD is the combination of static and dynamic control. While the underlying computational pipelines are con gured statically, a limited amount of dynamic control is provided which greatly increases the range and capability of applications that can be mapped to RaPiD. This paper illustrates this mapping and con guration for several important applications including a FIR lter, 2-D DCT, motion estimation, and parametric curve generation; it also shows how static and dynamic control are used to perform complex computations.