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Neuromorphic-based metaheuristics: A new generation of low power, low latency and small footprint optimization algorithms

El-ghazali Talbi

TL;DR

The paper argues that neuromorphic computing offers a compelling, energy-efficient alternative to traditional Von Neumann architectures for solving optimization problems via metaheuristics. It proposes a unified framework (Nheuristics) that leverages spike-based information encoding, SNN architectures, and hardware-aware design to implement greedy, local-search, swarm intelligence, and evolutionary strategies on neuromorphic platforms. It surveys neuron models, encoding schemes, hardware architectures, and simulation tools, and discusses computational complexity, energy consumption, and implementation challenges, including mapping, co-design, and scalability. The work highlights the potential for low latency, small footprint optimization, and edge deployment, while outlining research directions such as hierarchical/decomposition approaches, multi-objective optimization, and integration with emerging devices and heterogeneous HPC systems.

Abstract

Neuromorphic computing (NC) introduces a novel algorithmic paradigm representing a major shift from traditional digital computing of Von Neumann architectures. NC emulates or simulates the neural dynamics of brains in the form of Spiking Neural Networks (SNNs). Much of the research in NC has concentrated on machine learning applications and neuroscience simulations. This paper investigates the modelling and implementation of optimization algorithms and particularly metaheuristics using the NC paradigm as an alternative to Von Neumann architectures, leading to breakthroughs in solving optimization problems. Neuromorphic-based metaheuristics (Nheuristics) are supposed to be characterized by low power, low latency and small footprint. Since NC systems are fundamentally different from conventional Von Neumann computers, several challenges are posed to the design and implementation of Nheuristics. A guideline based on a classification and critical analysis is conducted on the different families of metaheuristics and optimization problems they address. We also discuss future directions that need to be addressed to expand both the development and application of Nheuristics.

Neuromorphic-based metaheuristics: A new generation of low power, low latency and small footprint optimization algorithms

TL;DR

The paper argues that neuromorphic computing offers a compelling, energy-efficient alternative to traditional Von Neumann architectures for solving optimization problems via metaheuristics. It proposes a unified framework (Nheuristics) that leverages spike-based information encoding, SNN architectures, and hardware-aware design to implement greedy, local-search, swarm intelligence, and evolutionary strategies on neuromorphic platforms. It surveys neuron models, encoding schemes, hardware architectures, and simulation tools, and discusses computational complexity, energy consumption, and implementation challenges, including mapping, co-design, and scalability. The work highlights the potential for low latency, small footprint optimization, and edge deployment, while outlining research directions such as hierarchical/decomposition approaches, multi-objective optimization, and integration with emerging devices and heterogeneous HPC systems.

Abstract

Neuromorphic computing (NC) introduces a novel algorithmic paradigm representing a major shift from traditional digital computing of Von Neumann architectures. NC emulates or simulates the neural dynamics of brains in the form of Spiking Neural Networks (SNNs). Much of the research in NC has concentrated on machine learning applications and neuroscience simulations. This paper investigates the modelling and implementation of optimization algorithms and particularly metaheuristics using the NC paradigm as an alternative to Von Neumann architectures, leading to breakthroughs in solving optimization problems. Neuromorphic-based metaheuristics (Nheuristics) are supposed to be characterized by low power, low latency and small footprint. Since NC systems are fundamentally different from conventional Von Neumann computers, several challenges are posed to the design and implementation of Nheuristics. A guideline based on a classification and critical analysis is conducted on the different families of metaheuristics and optimization problems they address. We also discuss future directions that need to be addressed to expand both the development and application of Nheuristics.

Paper Structure

This paper contains 27 sections, 8 equations, 13 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Neuromorphic computing
  3. Heuristics and optimization problems
  4. Design of Nheuristics
  5. Neuron model
  6. Information encoding
  7. Rate encoding
  8. Temporal encoding
  9. SNN architectures
  10. Learning rules
  11. Computational complexity of Nheuristics
  12. Implementation of Nheuristics
  13. Neuromorphic hardware
  14. Digital neuromorphic architecture
  15. Analog neuromorphic architecture
  16. ...and 12 more sections

Figures (13)

  • Figure 1: The principle of a spiking neuron in SNNs.
  • Figure 2: Comparison of neuron models in terms of biological plausibility and computational complexity.
  • Figure 3: Temporal diagram showing the number of emitted spikes based on the type of encoding used in SNNs: rate encoding, time encodings TTFS and ISI.
  • Figure 4: Feedforward and recurrent SNN architectures.
  • Figure 7: Mapping an Nheuristic on a given NC hardware: SNN partitioning in clusters, and cluster mapping on multiple cores of a single chip.
  • ...and 8 more figures