Multidimensional dataflow graphs

In many signal processing applications, the tokens in a stream of tokens have a dimension higher than one. For example, the tokens in a video stream represent images so that a video application is actually three- or four-dimensional: Two dimensions are required in order to describe the pixel coordinates, one dimension indexes the different color components, and the time finally corresponds to the last dimension. Static multidimensional (MD) streaming applications can be modeled using one-dimensional dataflow graphs[7], but these are at best cyclostatic dataflow graphs, often with many phases in the actor’s vector valued token production and consumption patterns. These models incur a high control overhead. Furthermore such a notation hides many important algorithm properties such as inherent data parallelism, fine grained data dependencies and thus required memory sizes. Finally, the model is very implementation specific in that some of the degrees of freedom such as the processing order are already nailed down and cannot be changed easily without completely recreating the model

Engineering Multirate Convolutions for Radar Imaging

We present a schematic design methodology for multirate convolution systems, based on combined algorithmic development and architecture design. It allows us to map the algebraic specification of a long convolution algorithm directly onto efficient fast convolution hardware based on short FFT processor elements or dedicated VLSI processors. The design methodology exploits the known relationship between multirate filter banks and fast convolution schemes in an implicit manner, and allows the hardware designer to concentrate on typical application specific constraints such as processing speed, processor size and memory utilization. The methodology has proven its usefulness in the design of a convolution processor for real-time on-board synthetic aperture radar imagin

Context-Aware Process Networks

In industry, embedded systems for stream-based processing are often modelled and verified by using process networks, such as Kahn process networks. An advantage of Kahn networks is that they allow asynchronous operation of process components in a network. A problem in these networks, however, is that asynchronously interfering events cannot be handled properly because they are intrinsically indeterminate and therefore destroy the compositional properties of the network. In this paper, we propose to extend the Kahn model of computations with a simple indeterminate construct. We call the resulting network a context-aware process network (CAPN). We show that these networks are capable of handling certain classes of events and can still be reduced to a class of parameterised Kahn networks