Efficient Approximate Checkerboard K-SVD for Resource Constrained Embedded Systems

K-Singular Value Decomposition (K-SVD) is one of the key techniques in many signal processing and machine learning applications, such as image denoising, principal component analysis and dictionary learning.It is challenge to implement real-time K-SVD on resource constrained embedded systems, due to its exponentially increased computational complexity for large scale image or data dimensions.Approximate computing, as a trending paradigm in saving size, weight, and power (SWaP) in computation, trades the accuracy with complexity, so as to enable energy efficiency in modern computing systems.In this work, an approximate K-SVD based Dictionary Learning is studied primarily with sub-divided data blocks using reduced precision.The scalability of checkerboard K-SVD performance and arithmetic approximation are quantified with reconstructed image quality and computational complexity.It enables the efficient implementation by showing up to 16x estimated speed up and 98% reduction in memory foot-print using checkerboard division, and averagely 37% memory foot-print savings with approximate computing.