REVAMP: A Systematic Framework for Heterogeneous CGRA Realization

Coarse-Grained Reconfigurable Architectures (CGRAs) provide an excellent balance between performance, energy efficiency, and flexibility. However, increasingly sophisticated applications, especially on the edge devices, demand even better energy efficiency for longer battery life. Most CGRAs adhere to a canonical structure where a homogeneous set of processing elements and memories communicate through a regular interconnect due to the simplicity of the design. Unfortunately, the homogeneity leads to substantial idle resources while mapping irregular applications and creates inefficiency. We plan to mitigate the inefficiency by systematically and judiciously introducing heterogeneity in CGRAs in tandem with appropriate compiler support. We propose REVAMP, an automated design space exploration framework that helps architects uncover and add pertinent heterogeneity to a diverse range of originally homogeneous CGRAs when fed with a suite of target applications. REVAMP explores a comprehensive set of optimizations encompassing compute, network, and memory heterogeneity, thereby converting a uniform CGRA into a more irregular architecture with improved energy efficiency. As CGRAs are inherently software scheduled, any micro-architectural optimizations need to be partnered with corresponding compiler support, which is challenging with heterogeneity. The REVAMP framework extends compiler support for efficient mapping of loop kernels on the derived heterogeneous CGRA architectures. We showcase REVAMP on three state-of-the-art homogeneous CGRAs, demonstrating how REVAMP derives a heterogeneous variant of each homogeneous architecture, with its corresponding compiler optimizations. Our results show that the derived heterogeneous architectures achieve up to 52.4% power reduction, 38.1% area reduction, and 36% average energy reduction over the corresponding homogeneous versions with minimal performance impact for the selected kernel suite.

Automated summary

This research aims to improve energy efficiency in CGRAs by introducing heterogeneity and corresponding compiler support. The study proposes an automated design space exploration framework, REVAMP, which converts homogeneous CGRAs into irregular architectures through optimizing compute, network, and memory heterogeneity. The research showcases REVAMP on three homogeneous CGRAs, demonstrating its effectiveness.