{"paper":{"title":"KPynq: A Work-Efficient Triangle-Inequality based K-means on FPGA","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DC","authors_text":"Boyuan Feng, Lei Deng, Yufei Ding, Yuke Wang, Zhaorui Zeng","submitted_at":"2019-05-22T19:41:17Z","abstract_excerpt":"K-means is a popular but computation-intensive algorithm for unsupervised learning. To address this issue, we present KPynq, a work-efficient triangle-inequality based K-means on FPGA for handling large-size, high-dimension datasets. KPynq leverages an algorithm-level optimization to balance the performance and computation irregularity, and a hardware architecture design to fully exploit the pipeline and parallel processing capability of various FPGAs. In the experiment, KPynq consistently outperforms the CPU-based standard K-means in terms of its speedup (up to 4.2x) and significant energy-ef"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.09345","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}