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Energy-Efficient Spike-Based Scalable Architecture for Next-Generation Cognitive AI Computing Systems

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Ubiquitous Networking (UNet 2021)

Part of the book series: Lecture Notes in Computer Science ((LNCCN,volume 12845))

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Abstract

In recent years, neuromorphic computing systems have taken a range of design approaches to exploit known computational principles of cognitive neuro-biological systems. Profiting from the brain’s event-driven nature modeled in spiking neural networks (SNN), these systems have been able to reduce power consumption. However, as neuromorphic systems require high integration to ensemble a functional silicon brain-like, moving to 3D integrated circuits (3D-ICs) with the three-dimensional network on chip (3D-NoC) interconnect is a suitable approach that allows for scalable designs, lower communication cost, and lower power consumption. This paper presents the design and evaluation of an energy-efficient spike-based scalable neuromorphic architecture. Evaluation results on MNIST classification, using the K-means-based multicast routing algorithm (KMCR), show that the proposed system maintains high accuracy with a small spike arrival window over various configurations.

This work is supported by the University of Aizu, Competitive Research Funding (CRF), Ref. UoA-P6-2020.

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Authors and Affiliations

  1. Adaptive Systems Laboratory, Graduate School of Computer Science and Engineering, The University of Aizu, Aizu-Wakamatsu, Fukushima, 965-8580, Japan

    Ogbodo Mark Ikechukwu, Khanh N. Dang & Abderazek Ben Abdallah

  2. SISLAB, University of Engineering and Technology, Vietnam National University Hanoi, Hanoi, 123106, Vietnam

    Khanh N. Dang

Authors
  1. Ogbodo Mark Ikechukwu
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  2. Khanh N. Dang
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  3. Abderazek Ben Abdallah
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Corresponding author

Correspondence to Ogbodo Mark Ikechukwu .

Editor information

Editors and Affiliations

  1. University of Quebec at Montreal (UQAM), Motreal, QC, Canada

    Halima Elbiaze

  2. ENSEM, Hassan II University of Casablanca, Casablanca, Morocco

    Essaid Sabir

  3. Universidad Publica de Navarra (UPNA), Pampelune, Spain

    Francisco Falcone

  4. ENSEM, Hassan II University of Casablanca, Casablanca, Morocco

    Mohamed Sadik

  5. University of Lorraine, Nancy, France

    Samson Lasaulce

  6. Sorbonne University, Villetaneuse, France

    Jalel Ben Othman

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Ikechukwu, O.M., Dang, K.N., Ben Abdallah, A. (2021). Energy-Efficient Spike-Based Scalable Architecture for Next-Generation Cognitive AI Computing Systems. In: Elbiaze, H., Sabir, E., Falcone, F., Sadik, M., Lasaulce, S., Ben Othman, J. (eds) Ubiquitous Networking. UNet 2021. Lecture Notes in Computer Science(), vol 12845. Springer, Cham. https://doi.org/10.1007/978-3-030-86356-2_19

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  • DOI: https://doi.org/10.1007/978-3-030-86356-2_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-86355-5

  • Online ISBN: 978-3-030-86356-2

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