Accelerator for Sparse Machine Learning
Leonid Yavits, Ran Ginosar · IEEE Computer Architecture Letters · 2017
Sparse matrix by vector multiplication (SpMV) plays a pivotal role in machine learning and data mining. We propose and investigate an SpMV accelerator, specifically designed to accelerate the sparse matrix by sparse vector multiplication (SpMSpV), and to be integrated in a CPU core. We show that our accelerator outperforms a similar solution by 70x while achieving 8x higher power efficiency, which yields an estimated 29x energy reduction for SpMSpV based applications.