  Large-scale eigenvalue computations on sparse matrices are a key component of
graph analytics techniques based on spectral methods. In such applications, an
exhaustive computation of all eigenvalues and eigenvectors is impractical and
unnecessary, as spectral methods can retrieve the relevant properties of
enormous graphs using just the eigenvectors associated with the Top-K largest
eigenvalues.
  In this work, we propose a hardware-optimized algorithm to approximate a
solution to the Top-K eigenproblem on sparse matrices representing large graph
topologies. We prototype our algorithm through a custom FPGA hardware design
that exploits HBM, Systolic Architectures, and mixed-precision arithmetic. We
achieve a speedup of 6.22x compared to the highly optimized ARPACK library
running on an 80-thread CPU, while keeping high accuracy and 49x better power
efficiency.
