A Hardware/Software Co-design Method for Approximate Semi-Supervised K-Means Clustering
A Hardware/Software Co-design Method for Approximate Semi-Supervised K-Means Clustering
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DOI：
10.1109/isvlsi.2018.00110
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发表时间：
2018-07
期刊：
影响因子：
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通讯作者：
Pengfei Huang;Chenghua Wang;Ruizhe Ma;Weiqiang Liu;F. Lombardi
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文献类型：
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作者：
Pengfei Huang;Chenghua Wang;Ruizhe Ma;Weiqiang Liu;F. Lombardi
作为最有前途的节能数字系统设计模式之一，近似计算近年来引起了人们的广泛关注。使用近似计算的应用程序可以容忍计算结果中的一些质量损失，以获得高性能。近似算术电路已经得到了广泛的研究，但在系统级上的应用还没有得到广泛的追求。此外，当在系统级应用近似算术电路时，计算中可能会出现误差累积效应和收敛问题。半监督学习通过使用未标记的样本来提高准确率和性能。提出了一种近似半监督k-均值聚类的软硬件协同设计方法。在学习过程的每一次迭代中，它利用特征约束来指导不同精度水平的近似计算。与基线设计相比，该方法降低了67%以上的功率延迟乘积，而引入的精度损失很小。图像分割的实例验证了该方法的有效性。
As one of the most promising energy-efficient emerging paradigms for designing digital systems, approximate computing has attracted a significant attention in recent years. Applications utilizing approximate computing can tolerate some loss of quality in the computed results for attaining high performance. Approximate arithmetic circuits have been extensively studied; however, their application at system level has not been extensively pursued. Furthermore, when approximate arithmetic circuits are applied at system level, error-accumulation effects and a convergence problem may occur in computation. Semi-supervised learning can improve accuracy and performance by using unlabeled examples. In this paper, a hardware/software co-design method for approximate semi-supervised k-means clustering is proposed. It makes use of feature constraints to guide the approximate computation at various accuracy levels in each iteration of the learning process. Compared with a baseline design, the proposed method reduces the power-delay product by over 67% while only a small loss of accuracy is introduced. A case study of image segmentation validates the effectiveness of the proposed method.