  Sparse matrix-vector multiplication is often employed in many data-analytic
workloads in which low latency and high throughput are more valuable than exact
numerical convergence. FPGAs provide quick execution times while offering
precise control over the accuracy of the results thanks to reduced-precision
fixed-point arithmetic. In this work, we propose a novel streaming
implementation of Coordinate Format (COO) sparse matrix-vector multiplication,
and study its effectiveness when applied to the Personalized PageRank
algorithm, a common building block of recommender systems in e-commerce
websites and social networks. Our implementation achieves speedups up to 6x
over a reference floating-point FPGA architecture and a state-of-the-art
multi-threaded CPU implementation on 8 different data-sets, while preserving
the numerical fidelity of the results and reaching up to 42x higher energy
efficiency compared to the CPU implementation.

Distributed, Parallel, and Cluster Computing;Hardware Architecture