TensorSVM

Abstract

This paper explores the use of Tensor Engines to accelerate nonlinear and linear SVM training. Support Vector Machine(SVM) is a classical machine learning model for classification and regression and remains to be the state-of-the-art model for some tasks such as text classification and bioinformatics. However large scale SVM training is still challenging because of its high computational complexity. This is especially severe for non-linear SVM with kernel tricks. On the other hand, the surging importance of neural networks fuels the emergence of specialized processors called Tensor Units (TensorCore in GPU and Tensor Processing Unit of Google) which are characterized by extreme efficiency and very limited precision and range. This paper proposes a TensorCore GPU based SVM algorithm and software system that is faster and more scalable than state-of-the-art SVM solvers. It includes a fast, accurate low-rank Gram matrix approximation that effectively utilizes the TensorCore in GPU and a primal-dual interior-point method to solve the quadratic program with a fast and predictable convergence rate. The random projection based Gram matrix approximation can be substantially accelerated by TensorCore on GPU. This exploration ends up with a tale of randomized numerical linear algebra, convex optimization, and high performance computing on Tensor Engines. Particularly, this paper suggests that the emerging randomized numerical linear algebra algorithms and Tensor Engines are synergistic in opening up exciting new application areas that include statistical machine learning and the wider scientific/engineering computing.