Low energy sketching engines on many-core platform for big data acceleration

摘要

Almost 90% of the data available today was created within the last couple of years, thus Big Data set processing is of utmost importance. Many solutions have been investigated to increase processing speed and memory capacity, however I/O bottleneck is still a critical issue. To tackle this issue we adopt Sketching technique to reduce data communications. Reconstruction of the sketched matrix is performed using Orthogonal Matching Pursuit (OMP). Additionally we propose Gradient Descent OMP (GD-OMP) algorithm to reduce hardware complexity. Big data processing at real-time imposes rigid constraints on sketching kernel, hence to further reduce hardware overhead both algorithms are implemented on a low power domain specific many-core platform called Power Efficient Nano Clusters (PENC). GD-OMP algorithm is evaluated for image reconstruction accuracy and the PENC many-core architecture. Implementation results show that for large matrix sizes GD-OMP algorithm is 1.3× faster and consumes 1.4× less energy than OMP algorithm implementations. Compared to GPU and Quad-Core CPU implementations the PENC many-core reconstructs 5.4× and 9.8× faster respectively for large signal sizes with higher sparsity.