Affiliation of Author(s):电子信息工程学院

Journal:2018 IEEE COMPUTER SOCIETY ANNUAL SYMPOSIUM ON VLSI (ISVLSI)

Key Words:Approximate computing semi-supervised learning approximate multiplier K-means clustering

Abstract: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.

ISSN No.:2159-3469

Translation or Not:no

Date of Publication:2018-01-01

Co-author:Wang Chenghua,Ma, Ruizhe,Weiqiang Liu,Lombardi, Fabrizio

Correspondence Author:huangpengfei