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This chapter presents the design and evaluation of the reliable three-dimensional digital neuromorphic processor (R-NASH), which is built to mimic the 3D structure of biological brains using 3D-integrated circuits (3D-ICs). The platform achieves high integration density and minimizes spike transmission delays in spiking neural networks, offering scalability for diverse applications. R-NASH leverages Through-Silicon-Via (TSV) technology to implement spiking neural networks across neuron clusters via a Network-on-Chip (NoC) architecture. In addition to the core processing capabilities, R-NASH features an advanced memory interface that connects seamlessly to a host CPU. This integration enables real-time training and inference of neural networks, significantly enhancing the system’s operational efficiency and flexibility. The processor’s design ensures high-speed communication between the neuron clusters and the central processing unit, facilitating rapid and accurate data processing essential for complex neural computations. Furthermore, R-NASH incorporates sophisticated fault detection and recovery mechanisms. These mechanisms are crucial for maintaining system reliability, allowing the processor to detect and address errors dynamically. The system ensures continuous operation despite faults by implementing strategies for gradual performance degradation rather than abrupt failures. This robustness is essential for applications in critical environments where downtime or performance lapses can have significant consequences. Overall, this chapter provides a comprehensive overview of the practical design and implementation of the R-NASH system, highlighting its innovative use of 3D-IC technology and fault-tolerant features. The detailed analysis demonstrates how R-NASH balances high performance, reliability, and scalability, making it a versatile solution for modern neuromorphic computing applications. To reinforce the concepts discussed, the chapter concludes with exercises facilitating deeper understanding and practical application of the material.

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Practical Design of a Reconfigurable 3D-NoC-Based Neuromorphic System

  • 章节

  • 发表日期：

    2025年

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  • Abderazek Ben Abdallah,
  • Khanh N. Dang

摘要

This chapter presents the design and evaluation of the reliable three-dimensional digital neuromorphic processor (R-NASH), which is built to mimic the 3D structure of biological brains using 3D-integrated circuits (3D-ICs). The platform achieves high integration density and minimizes spike transmission delays in spiking neural networks, offering scalability for diverse applications. R-NASH leverages Through-Silicon-Via (TSV) technology to implement spiking neural networks across neuron clusters via a Network-on-Chip (NoC) architecture. In addition to the core processing capabilities, R-NASH features an advanced memory interface that connects seamlessly to a host CPU. This integration enables real-time training and inference of neural networks, significantly enhancing the system’s operational efficiency and flexibility. The processor’s design ensures high-speed communication between the neuron clusters and the central processing unit, facilitating rapid and accurate data processing essential for complex neural computations. Furthermore, R-NASH incorporates sophisticated fault detection and recovery mechanisms. These mechanisms are crucial for maintaining system reliability, allowing the processor to detect and address errors dynamically. The system ensures continuous operation despite faults by implementing strategies for gradual performance degradation rather than abrupt failures. This robustness is essential for applications in critical environments where downtime or performance lapses can have significant consequences. Overall, this chapter provides a comprehensive overview of the practical design and implementation of the R-NASH system, highlighting its innovative use of 3D-IC technology and fault-tolerant features. The detailed analysis demonstrates how R-NASH balances high performance, reliability, and scalability, making it a versatile solution for modern neuromorphic computing applications. To reinforce the concepts discussed, the chapter concludes with exercises facilitating deeper understanding and practical application of the material.