ILP Based Mapping for Elastic CGRAs

In recent years, the emergence of deep learning and the need for big data analysis have created a demand for computers with high computational performance and energy efficiency. Since conventional ASICs and general-purpose CPUs cannot meet this requirement, domain-specific architectures that constrain applications are currently the focus of attention. Coarse-Grained Reconfigurable Architecture (CGRA), one of the domain-specific architectures, is attracting attention because it is superior to CPUs and FPGAs in terms of computational performance and power efficiency [1]. As depicted in Fig. 1, CGRA comprises a two-dimensional array of Processing Elements (PEs) and provides flexibility to change the instructions executed on the PEs and the connections between them, depending on the software being executed. On the other hand, the mapping problem for CGRA is an NP-complete problem, and there exist tradeoffs between solution accuracy and execution time. For real time applications, it is indispensable to shorten the mapping time. Thus, in this study, we propose an Integer Linear Programming (ILP) based mapping method for Elastic CGRA that aims to shorten mapping time while preserving solution accuracy. We have also implemented and preliminary evaluated the proposed method.