A sparse matrix vector multiply accelerator for support vector machine

Sparse matrix vector multiplication (SpMV) is a linear algebra construct commonly found in machine learning (ML) algorithms, such as support vector machine (SVM). We profiled a popular SVM software (libSVM) on an energy-efficient microserver and a high-performance server for real-world ML datasets, and observed that SpMV dominates runtime. We propose a novel SpMV algorithm tailored for ML and a hardware accelerator architecture design based on this algorithm. Our evaluations show that the proposed algorithm and hardware accelerator achieves significant efficiency improvements over the conventional SpMV algorithm used in libSVM.