QR-PULP: Streamlining QR Decomposition for RISC-V Parallel Ultra-Low-Power Platforms

QR decomposition is a numerical method used in many applications from the High-Performance Computing (HPC) domain to embedded systems. This broad spectrum of applications has drawn academic and commercial attention to developing many software libraries and domain-specific hardware solutions. In the Internet of Things (IoT) domain, multicore Parallel Ultra-Low-Power (PULP) architectures are emerging as energy-efficient alternatives, outperforming conventional single-core devices by coupling parallel processing with near-threshold computing. To the best of the authors' knowledge, our study introduces the first parallelized and optimized implementation of three distinct QR decomposition methods (Givens rotations, Gram-Schmidt process, and Householder transformation) on GAP-9, a commercial embodiment of the PULP architecture. Parallel execution on the 8-core cluster leads to a reduction in the total number of cycles by 241% for Givens rotations, 470% for Gram-Schmidt, and 567% for Householder, compared to the GAP9 1-core scenario. while each of them only consumes 0.013 mJ, 0.012 mJ, and 0.216 mJ, respectively. Compared to traditional single-core architectures based on ARM architectures, we achieve 8×, 24×, and 30× better performance and 36×, 35×, and 30× better energy efficiency, paving the way for broad adoption of complex linear algebra tasks in the IoT domain.