| 書目名稱 | Introduction to Noise-Resilient Computing | | 編輯 | S.N. Yanushkevich,G. Tangim,S. Kasai | | 視頻video | http://file.papertrans.cn/474/473956/473956.mp4 | | 叢書名稱 | Synthesis Lectures on Digital Circuits & Systems | | 圖書封面 |  | | 描述 | Noise abatement is the key problem of small-scaled circuit design. New computational paradigms are needed -- as these circuits shrink, they become very vulnerable to noise and soft errors. In this lecture, we present a probabilistic computation framework for improving the resiliency of logic gates and circuits under random conditions induced by voltage or current fluctuation. Among many probabilistic techniques for modeling such devices, only a few models satisfy the requirements of efficient hardware implementation -- specifically, Boltzman machines and Markov Random Field (MRF) models. These models have similar built-in noise-immunity characteristics based on feedback mechanisms. In probabilistic models, the values 0 and 1 of logic functions are replaced by degrees of beliefs that these values occur. An appropriate metric for degree of belief is probability. We discuss various approaches for noise-resilient logic gate design, and propose a novel design taxonomy based on implementation of the MRF model by a new type of binary decision diagram (BDD), called a cyclic BDD. In this approach, logic gates and circuits are designed using 2-to-1 bi-directional switches. Such circuits are | | 出版日期 | Book 2013 | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-031-79855-9 | | isbn_softcover | 978-3-031-79854-2 | | isbn_ebook | 978-3-031-79855-9Series ISSN 1932-3166 Series E-ISSN 1932-3174 | | issn_series | 1932-3166 | | copyright | Springer Nature Switzerland AG 2013 |
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