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System-level integration in neuromorphic co-processors


Indiveri, Giacomo; Linares-Barranco, Bernabé; Payvand, Melika (2020). System-level integration in neuromorphic co-processors. In: Spiga, Sabina; Sebastian, Abu; Querlioz, Damien; Rajendran, Bipin. Memristive Devices for Brain-Inspired Computing. Cambridge: Elsevier, 479-497.

Abstract

In this chapter we present results on system-level integration of memristive devices with neuromorphic circuits and systems. Specifically we present an overview of the current state-of-the-art hybrid memristive-complementary metal–oxide–semiconductor (CMOS) mixed-signal neuromorphic circuits for learning and plasticity and present perspectives toward integration of memristive devices in neuromorphic spiking neural network architectures. We focus on neuromorphic circuits and architectures that allow for a relatively natural integration of memristive devices, irrespective of the specific characteristics of the specific memristive device technology adopted. We address the cointegration of memristive devices with on-chip learning mechanisms, using both analog and digital CMOS circuits, to build a solid background of the functionality of neuromorphic circuits explaining how memristive devices can be implemented on them. Furthermore we also address the system-level integration of such architectures in multicore and multichip systems, for connecting them to input and output devices, such as sensors, actuators, and conventional CMOS processing devices.

Abstract

In this chapter we present results on system-level integration of memristive devices with neuromorphic circuits and systems. Specifically we present an overview of the current state-of-the-art hybrid memristive-complementary metal–oxide–semiconductor (CMOS) mixed-signal neuromorphic circuits for learning and plasticity and present perspectives toward integration of memristive devices in neuromorphic spiking neural network architectures. We focus on neuromorphic circuits and architectures that allow for a relatively natural integration of memristive devices, irrespective of the specific characteristics of the specific memristive device technology adopted. We address the cointegration of memristive devices with on-chip learning mechanisms, using both analog and digital CMOS circuits, to build a solid background of the functionality of neuromorphic circuits explaining how memristive devices can be implemented on them. Furthermore we also address the system-level integration of such architectures in multicore and multichip systems, for connecting them to input and output devices, such as sensors, actuators, and conventional CMOS processing devices.

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Additional indexing

Item Type:Book Section, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Neuroinformatics
Dewey Decimal Classification:570 Life sciences; biology
Language:English
Date:1 January 2020
Deposited On:15 Feb 2021 15:28
Last Modified:15 Feb 2021 15:28
Publisher:Elsevier
ISBN:9780081027820
OA Status:Closed
Publisher DOI:https://doi.org/10.1016/b978-0-08-102782-0.00018-6

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