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Reconfigurable Memristor Fabrics for Heterogeneous Computing

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Advances in Neuromorphic Memristor Science and Applications

Part of the book series: Springer Series in Cognitive and Neural Systems ((SSCNS,volume 4))

Abstract

Device scaling and performance limits in complementary metal-oxide-semiconductor (CMOS) technology are leading to a rapid proliferation of novel computing paradigms for information processing. To this end, CMOS/memristor hybrid technology, where memristive devices are integrated in 3D, is extremely promising. These technologies are emerging under the joint efforts of industry and academe, with innovations in fabrication processes, materials, devices, and circuits. A prime advantage of such computing technology is its ability to offer tera-bit densities with ultra low power and long data retention times. These unique characteristics motivate the development of computational fabrics that can dynamically transform over time to perform heterogeneous computing based on system requirements, with the technological objective of superseding classical CMOS architectures. This book chapter discusses the different models of memristors which can be used for implementing memristors as memory, sensing, logic and nueromorphic units.

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Author information

Authors and Affiliations

  1. Department of Computer Engineering, Rochester Institute of Technology, Rochester, NY, USA

    Dhireesha Kudithipudi & Cory E. Merkel

Authors
  1. Dhireesha Kudithipudi
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  2. Cory E. Merkel
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Correspondence to Dhireesha Kudithipudi .

Editor information

Editors and Affiliations

  1. Dept. Mathematical Sciences, University of Memphis, Memphis, 38152, Tennessee, USA

    Robert Kozma

  2. , RITC, Information Directorate, Air Force Research Laboratory, Electronics Parkway 26, Rome, 13441-4514, New York, USA

    Robinson E. Pino

  3. , Mathematical Sciences, The University of Memphis, Dunn Hall 373, Memphis, 38152, Tennessee, USA

    Giovanni E. Pazienza

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Kudithipudi, D., Merkel, C.E. (2012). Reconfigurable Memristor Fabrics for Heterogeneous Computing. In: Kozma, R., Pino, R., Pazienza, G. (eds) Advances in Neuromorphic Memristor Science and Applications. Springer Series in Cognitive and Neural Systems, vol 4. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-4491-2_7

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