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Reconfigurable Neuromorphic Computing System

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Neuromorphic Computing Principles and Organization

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Abstract

The human brain can be characterized by its massive parallel reconfigurable synapses connecting billions of neurons. Synapses play a vital role in achieving the learning and adaptability of the human brain. The weight of a synapse shows connection strength between the two neurons linked by that synapse. Spiking neural networks are used in applications ranging from vision systems to brain-computer interfaces. However, the design of such systems has mainly focused on fixed functionality using available off-the-shelf components. Such an approach is lacking the flexibility to adapt to various computing environments. The reconfigurable design approach supports multiple target applications via dynamic reconfigurability, network topology independence, and network expandability. This chapter presents the architecture and hardware design of a reconfigurable neuromorphic processor. The architecture implements a spiking neural network that can be reconfigured to recover from faults with suitable methods that use an FPGA without being dependent on FPGA intellectual property. This approach makes possible its implementation in Application-Specific Integrated Circuits (ASICs).

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Authors and Affiliations

  1. University of Aizu, Aizu-Wakamatsu, Japan

    Abderazek Ben Abdallah

  2. University of Aizu, Aizu-Wakamatsu, Japan

    Khanh N. Dang

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  1. Abderazek Ben Abdallah
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  2. Khanh N. Dang
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Correspondence to Abderazek Ben Abdallah or Khanh N. Dang .

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Ben Abdallah, A., N. Dang, K. (2022). Reconfigurable Neuromorphic Computing System. In: Neuromorphic Computing Principles and Organization. Springer, Cham. https://doi.org/10.1007/978-3-030-92525-3_7

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  • DOI: https://doi.org/10.1007/978-3-030-92525-3_7

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