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Neuromorphic System Design Fundamentals

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Neuromorphic Computing Principles and Organization

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Abstract

The neuromorphic computing paradigm has the potential to improve the efficiency of computational tasks. Unlike the typical artificial neural networks (ANNs), where neurons fire at each propagation cycle, the neurons in a neuromorphic neural networks model, named spiking neural networks (SNNs), fire only when their membrane potential reaches a certain threshold. Spiking neurons are only activated when sufficient signals are integrated from other neurons, which leads to sparse neural activities at the network level. Furthermore, their asynchronous event-driven operations, distributed memory, and massive parallelism significantly accelerate information processing and reduce energy consumption in many applications (i.e., pattern recognition, object detection, navigation, motor control, and so on). The key design challenges of neuromorphic systems include: how the organization of individual neurons, circuits, applications, and overall architectures enable energy-efficient computations, how information is represented, and how adaptation to local and evolutionary changes are facilitated. Moreover, a massively parallel neuromorphic architecture will require building small-sized neuro processing cores with low-power consumption, efficient neuron coding schemes, and a lightweight on-chip learning algorithm, which is also a challenges. This chapter covers fundamental design principles to build an efficient neuromorphic system in hardware.

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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). Neuromorphic System Design Fundamentals. In: Neuromorphic Computing Principles and Organization. Springer, Cham. https://doi.org/10.1007/978-3-030-92525-3_2

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  • DOI: https://doi.org/10.1007/978-3-030-92525-3_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-92524-6

  • Online ISBN: 978-3-030-92525-3

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