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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Providing an Adaptive Routing along with a Hybrid Selection Strategy to Increase Efficiency in NoC-Based Neuromorphic

Mohammad Trik1, Saadat Pour Mozaffari2, Amir Massoud Bidgoli1

  • 1Department of Computer Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran.

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This study introduces an adaptive routing algorithm for neuromorphic systems on chip (NoC). The novel approach reduces average delay and power consumption in network-on-chip communication.

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Area of Science:

  • Computer Engineering
  • Artificial Intelligence
  • VLSI Design

Background:

  • Efficient routing is crucial for network-on-chip (NoC) based neuromorphic systems.
  • Effective communication structures enhance scalability and reduce power consumption by minimizing global wiring.

Purpose of the Study:

  • To propose an adaptive routing algorithm with a hybrid selection strategy for NoC-based neuromorphic systems.
  • To reduce average delay time and power consumption in these systems.

Main Methods:

  • A traffic analyzer determines local or nonlocal traffic based on hop count.
  • A hybrid selection strategy employs RCA for nonlocal and NoP for local traffic.
  • Experiments were conducted in a simulator environment.

Main Results:

  • The proposed adaptive routing algorithm effectively reduces average delay.
  • The solution demonstrates a significant decrease in power consumption.
  • The hybrid selection strategy optimizes routing based on traffic type.

Conclusions:

  • The adaptive routing algorithm and hybrid selection strategy offer an efficient solution for NoC-based neuromorphic systems.
  • This approach enhances performance by minimizing delay and power usage.
  • The findings contribute to the development of scalable and power-efficient neuromorphic hardware.