ApproxEigen: An approximate computing technique for large-scale eigen-decomposition

Recognition, Mining, and Synthesis (RMS) applications are expected to make up much of the computing workloads of the future. Many of these applications (e.g., recommender systems and search engine) are formulated as finding eigenvalues/vectors of large-scale matrices. These applications are inherently error-tolerant, and it is often unnecessary, sometimes even impossible, to calculate all the eigenpairs. Motivated by the above, in this work, we propose a novel approximate computing technique for large-scale eigen-decomposition, namely ApproxEigen, wherein we focus on the practically-used Krylov subspace methods to find finite number of eigenpairs. With ApproxEigen, we provide a set of computation kernels with different levels of approximation for data pre-processing and solution finding, and conduct accuracy tuning under given quality constraints. Experimental results demonstrate that ApproxEigen is able to achieve significant energy-efficiency improvement while keeping high accuracy.