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Bayesian neural network enhancing reliability against conductance drift for memristor neural networks

Science China(Information Sciences) 2021年第6期64卷 100-111页

作者： Yue ZHOU Xiaofang HU Lidan WANG Shukai DUAN College of Electronics and Information Engineering Southwest University College of Artificial Intelligence Southwest University

The hardware implementation of neural networks based on memristor crossbar array provides a promising paradigm for neuromorphic computing. However, the existence of memristor conductance drift harms the reliability of the deployed neural network, which seriously hinders the practical application of memristor-based neuromorphic computing. In this paper, the impact of different types of conductance drift on the weight realized by memristors is investigated and analyzed. Then, utilizing the weight uncertainty introduced by conductance drift, we propose a weight optimization method based on the Bayesian neural network, which can greatly improve the network performance. Furthermore, an ensemble approach is proposed to enhance network reliability without increasing training cost or crossbar array resources. Finally, the effectiveness of the proposed scheme is verified through a series of experiments. In addition, the proposed scheme can be easily integrated into the implementation of neuromorphic computing, which can provide a better guarantee for its large-scale application.

关键词： conductance drift neuromorphic computing Bayesian neural network memristor crossbar array network reliability

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Single Image Super-Resolution via the Implementation of the Hardware-Friendly Sparse Coding

Single Image Super-Resolution via the Implementation of the ...

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第37届中国控制会议

作者： Zhekang Dong Chun Sing Lai Zhao Xu Donglian Qi College of Electrical Engineering Zhejiang University Department of Electrical Engineering The Hong Kong Polytechnic University Department of Engineering Science University of Oxford

Sparse representation is one of the effective approaches to perform the feature extraction on the high-dimensional signals and has been widely used in the field of image processing. In this paper, a novel single image super-resolution（SR） reconstruction algorithm based on sparse coding is presented. Specifically, due to the fact that sparse coding has the potential to greatly facilitate the biological-neural system and to deal with a large amount of complex data, while consuming a finite amount of energy, a memristor crossbar array synthesized by a threshold-type memristor model is constructed for the implementation of a hardware-friendly sparse coding method, i.e., the Locally Competitive Algorithm（LCA）. Meanwhile, the relevant online dictionary learning method is provided, along with the description of weight programming strategy. Furthermore, the specific description of the image SR via the LCA sparse coding is provided. For the verification purpose, a series of experiments are carried out to illustrate the validity and effectiveness of the entire scheme.

关键词： Image super-resolution sparse coding memristor crossbar array dictionary learning

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