GREEN: An Approximate SIMD/MIMD CGRA for Energy-Efficient ProcessiNg at the Edge

The rapid evolution of compute-intensive programs from bio-signal to image-, and video-processing has motivated moving toward Coarse Grained Reconfigurable Architectures (CGRAs), having high parallelism capability with post-fabrication datapath versatility. To enhance energy-efficiency of such error-resilient applications, State-of-the-Art (SoA) CGRAs exploit approximation techniques, while maintaining an acceptable accuracy for the final Quality of Result (QoR). However, such CGRAs suffer from overheads of utilizing separate Add/Mul/Div units. We propose GREEN as an energy-efficient CGRA, which enables synergistic effects of a chain of approximation and optimization techniques in various levels of abstraction, from application-, to architecture-, to circuit-level, in a cross-layer hierarchy. Enabling this, GREEN offers different levels of energy-accuracy trade-offs through the flexibility of its small Processing Elements (PEs), each of which can support various functionalities and precision-adaptability in a Single Instruction, Multiple Data (SIMD) or Multiple Instruction, Multiple Data (MIMD) manner. Experimental results obtained with Synopsys Design Compiler and Cadence Innovus at 45 nm CMOS technology node demonstrate the efficiency of the proposed SISD/SIMD/MIMD CGRA over the accurate and SoA counterparts. In particular, the MIMD mode of GREEN enables up to 6.6× higher throughput while dissipating 21% less energy than the accurate counterpart. Moreover, the end-to-end evaluation of GREEN variants on eight single-and multi-kernel applications from classification, bio-signal (ECG/EEG), and image/video processing domains demonstrates significant performance improvement, compared to the accurate CGRA. In particular, GREEN-MIMD not only speed-ups the ECG QRS detection by 49% and consumes 43% less area and 66% less energy than the accurate CGRA, but also maintains the heartbeat detection accuracy at 100%. GREEN implementations is available at https://cfaed.tu-dresden.de/pd-downloads.