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A recipe for creating ideal hybrid memristive-CMOS neuromorphic processing systems


Chicca, E; Indiveri, G (2020). A recipe for creating ideal hybrid memristive-CMOS neuromorphic processing systems. Applied Physics Letters, 116(12):120501.

Abstract

The development of memristive device technologies has reached a level of maturity to enable the design and fabrication of complex and large-scale hybrid memristive-Complementary Metal-Oxide Semiconductor (CMOS) neural processing systems. These systems offer promising solutions for implementing novel in-memory computing architectures for machine learning and data analysis problems. We argue that they are also ideal building blocks for integration in neuromorphic electronic circuits suitable for ultra-low power brain-inspired sensory processing systems, therefore leading to innovative solutions for always-on edge-computing and Internet-of-Things applications. Here, we present a recipe for creating such systems based on design strategies and computing principles inspired by those used in mammalian brains. We enumerate the specifications and properties of memristive devices required to support always-on learning in neuromorphic computing systems and to minimize their power consumption. Finally, we discuss in what cases such neuromorphic systems can complement conventional processing ones and highlight the importance of exploiting the physics of both the memristive devices and the CMOS circuits interfaced to them.
We wish to acknowledge Melika Payvand and Regina Dittmann for the constructive comments on this manuscript. The illustration of Fig. 1 was kindly provided by the University of Zurich, Information Technology, MELS/SIVIC, Sarah Steinbacher. This work was supported by the European Research Council (ERC) under the European Union's Horizon 2020 Research and Innovation Program Grant Agreement No. 724295 (NeuroAgents).

Abstract

The development of memristive device technologies has reached a level of maturity to enable the design and fabrication of complex and large-scale hybrid memristive-Complementary Metal-Oxide Semiconductor (CMOS) neural processing systems. These systems offer promising solutions for implementing novel in-memory computing architectures for machine learning and data analysis problems. We argue that they are also ideal building blocks for integration in neuromorphic electronic circuits suitable for ultra-low power brain-inspired sensory processing systems, therefore leading to innovative solutions for always-on edge-computing and Internet-of-Things applications. Here, we present a recipe for creating such systems based on design strategies and computing principles inspired by those used in mammalian brains. We enumerate the specifications and properties of memristive devices required to support always-on learning in neuromorphic computing systems and to minimize their power consumption. Finally, we discuss in what cases such neuromorphic systems can complement conventional processing ones and highlight the importance of exploiting the physics of both the memristive devices and the CMOS circuits interfaced to them.
We wish to acknowledge Melika Payvand and Regina Dittmann for the constructive comments on this manuscript. The illustration of Fig. 1 was kindly provided by the University of Zurich, Information Technology, MELS/SIVIC, Sarah Steinbacher. This work was supported by the European Research Council (ERC) under the European Union's Horizon 2020 Research and Innovation Program Grant Agreement No. 724295 (NeuroAgents).

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Neuroinformatics
Dewey Decimal Classification:570 Life sciences; biology
Scopus Subject Areas:Physical Sciences > Physics and Astronomy (miscellaneous)
Uncontrolled Keywords:Physics and Astronomy (miscellaneous)
Language:English
Date:23 March 2020
Deposited On:15 Feb 2021 15:26
Last Modified:16 Feb 2021 21:02
Publisher:American Institute of Physics
ISSN:1077-3118
OA Status:Closed
Publisher DOI:https://doi.org/10.1063/1.5142089
Project Information:
  • : FunderH2020
  • : Grant ID724295
  • : Project TitleNeuroAgents - Neuromorphic Electronic Agents: from sensory processing to autonomous cognitive behavior

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