PAxC: A Probabilistic-oriented Approximate Computing Methodology for ANNs
Pengfei Huanga, Chenghua Wangb, Ke Chenc and Weiqiang Liud
College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China
apfhuang@nuaa.edu.cn
bchwang@nuaa.edu.cn
cchen.ke@nuaa.edu.cn
dliuweiqiang@nuaa.edu.cn
ABSTRACT
In spite of the rapidly increasing number of approximate designs in circuit logic stack for Artificial Neural Networks (ANNs) learning. A principled and systematic approximate hardware incorporating domain knowledge is still lacking. As the layer of ANN becomes deeper, the errors introduced by approximate hardware will be accumulated quickly, which can result in unexpected results. In this paper, we propose a probabilisticoriented approximate computing (PAxC) methodology based on the notion of approximate probability to overcome the conceptual and computational difficulties inherent to probabilistic ANN learning. The PAxC makes use of minimum likelihood error in both circuit and application level to maintain the aggressive approximate datapaths to boost the benefits from the tradeoff between accuracy and energy. Compared with a baseline design, the proposed method significantly reduces the power-delay product (PDP) with a negligible accuracy loss. Simulation and a case study of image processing validate the effectiveness of the proposed methodology.
Keywords: Approximate computing, probabilistic-oriented, ANN learning, hybrid approximate circuits.
