STPAcc: Structural TI-Based Pruning for Accelerating Distance-Related Algorithms on CPU-FPGA Platforms

As a promising solution to boost the performance of distance-related algorithms (e.g.,
$K$
-means and KNN), FPGA-based acceleration attracts lots of attention, but also comes with numerous challenges. In this work, we propose,
STPAcc
, an optimization framework based on structural triangle-inequality (TI)-based pruning (STP) for accelerating distance-related algorithms on CPU-FPGA platforms. STPAcc provides a domain-specific language to unify distance-related algorithms effectively, a structural TI-based pruning strategy to remove unnecessary distance computations, a coarse-grained workload partitioning and mapping strategy to fully exploit the potentials of the CPU-FPGA platform, and fine-grained hardware optimizations to further improve performance on the FPGA. Intensive experiments show that STPAcc designs achieve
$31.42\times $
speedup and
$99.63\times $
better energy efficiency on average over standard CPU-based implementations.