A Dataflow Inspired Programming Paradigm for Coarse-Grained Reconfigurable Arrays

Abstract

In this paper, we present a new approach towards programming coarse-grained reconfigurable arrays (CGRAs) in an intuitive, dataflow inspired way. Based on the observation that available CGRAs are usually programmed using C, which lacks proper support for instruction-level parallelism, we instead started from a dataflow perspective combined with a language that inherently supports parallel structures. Our programming paradigm decouples the local functionality of a core from the global flow of data, i.e. the kernels from the routing. We will describe the ideas of our programming paradigm and also the language and compiler itself. Our complete system, including the CGRA, the programming language and the compiler, was developed using Haskell, which leads to a complete, sound system. We finish the paper with the implementation of a number of algorithms using our system. Motivation and Related WorkMany algorithms common in digital signal processing (DSP), like for example audio filtering, contain a high degree of instruction-level parallelism. To accelerate those algorithms, coarse-grained reconfigurable arrays (CGRAs) are often used due to their capability of large-scale parallelism. A CGRA is an array of small, configurable cores, often in combination with a general purpose processor for control operations. The cores in the CGRA usually contain an ALU and a small local memory.Popular examples of CGRAs are MorphoSys (2000) [1], XPP (2003) [2], ADRES (2003) [3] and SmartCell (2010) [4]. Since the details of the mentioned CGRAs is out of scope of this paper, the reader is referred to the respective papers and to the surveys [5] and [6], where a good overview on CGRAs is given.This research is conducted as part of the Sensor Technology Applied in Reconfigurable systems for sustainable Security (STARS) project (www.starsproject.nl)

Functionals and hardware

“…ere are also new trends in CGRA: data-ow graphs (Anja Niedermeier et al (2014) [37], and Francesca Palumbo et al (2016) [38]), full pipelining and dynamic composition (Jason Cong et al (2014) [7]), and overlay architecture (Davor Capalija et al (2014) [6], Abhishek Jain et al (2016) [24], and David Andrews et al (2016) [33]).…”