HiMap: Fast and Scalable High-Quality Mapping on CGRA via Hierarchical Abstraction

Abstract

Coarse-Grained Reconfigurable Array (CGRA) has emerged as a promising hardware accelerator due to the excellent balance between reconfigurability, performance, and energy efficiency. The performance of a CGRA strongly depends on the existence of a high-quality compiler to map the application kernels on the architecture. Unfortunately, the state-of-the-art compiler technology falls short in generating high-performance mapping within an acceptable compilation time, especially with increasing CGRA size. We propose HiMap – a fast and scalable CGRA mapping approach – that is also adept at producing close-to-optimal solutions for regular computational kernels prevalent in existing and emerging application domains. The key strategy behind HiMap’s efficiency and scalability is to exploit the regularity in the computation by employing a virtual systolic array as an intermediate abstraction layer in a hierarchical mapping. HiMap first maps the loop iterations of the kernel onto a virtual systolic array and then distills out the unique patterns in the mapping. These unique patterns are subsequently mapped onto sub-spaces of the physical CGRA. They are arranged together according to the systolic array mapping to create a complete mapping of the kernel. Experimental results confirm that HiMap can generate application mappings that hit the performance envelope of the CGRA. HiMap offers 17.3x and 5x improvement in performance and energy efficiency of the mappings compared to the state-of-the-art. The compilation time of HiMap for near-optimal mappings is less than 15 minutes for 64x64 CGRA, while existing approaches take days to generate inferior mappings.