We consider software-hardware acceleration of K-means clustering on the Intel Xeon+FPGA platform. We design a pipelined accelerator for K-means and combine it with CPU threads to assess performance benefits of (1) acceleration when data are only accessed from system memory and (2) cooperative CPU-FPGA acceleration. Our evaluation shows that the accelerator is up to 12.7×/2.4× faster than a single CPU thread for the assignment/update step of K-means. The cooperative use of threads and FPGA is roughly 1.9× faster than CPU threads alone or the FPGA by itself. Our approach delivers 4×–5× higher throughput compared to existing offload processing approaches.

中文翻译：

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紧耦合 CPU-FPGA 系统上 K-means 的协同软硬件加速

我们考虑在英特尔至强+FPGA 平台上对 K-means 集群进行软硬件加速。我们为 K-means 设计了一个流水线加速器，并将其与 CPU 线程相结合，以评估 (1) 仅从系统内存访问数据时的加速和 (2) CPU-FPGA 协作加速的性能优势。我们的评估表明，对于 K-means 的分配/更新步骤，加速器比单个 CPU 线程快 12.7 倍/2.4 倍。线程和 FPGA 的协同使用比单独使用 CPU 线程或单独使用 FPGA 快大约 1.9 倍。与现有的卸载处理方法相比，我们的方法提供了 4 倍至 5 倍的吞吐量。