R-Blocks: an Energy-Efficient, Flexible, and Programmable CGRA

Abstract

Emerging data-driven applications in the embedded, e-Health, and internet of things (IoT) domain require complex on-device signal analysis and data reduction to maximize energy efficiency on these energy-constrained devices. Coarse-grained reconfigurable architectures (CGRAs) have been proposed as a good compromise between flexibility and energy efficiency for ultra-low power (ULP) signal processing. Existing CGRAs are often specialized and domain-specific or can only accelerate simple kernels, which makes accelerating complete applications on a CGRA while maintaining high energy efficiency an open issue. Moreover, the lack of instruction set architecture (ISA) standardization across CGRAs makes code generation using current compiler technology a major challenge. This work introduces R-Blocks; a ULP CGRA with HW/SW co-design tool-flow based on the OpenASIP toolset. This CGRA is extremely flexible due to its well-established VLIW-SIMD execution model and support for flexible SIMD-processing, while maintaining an extremely high energy efficiency using software bypassing, optimized instruction delivery, and local scratchpad memories. R-Blocks is synthesized in a commercial 22-nm FD-SOI technology and achieves a full-system energy efficiency of 115 MOPS/mW on a common FFT benchmark, 1.45× higher than a highly tuned embedded RISC-V processor. Comparable energy efficiency is obtained on multiple complex workloads, making R-Blocks a promising acceleration target for general-purpose computing.