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

arXiv: https://arxiv.org/abs/2505.16362 · HTML: https://mlsanity.com/paper/2505.16362/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 30 of its 171 references that are in the map.
- First posted on arXiv 22 May 2025.

## Builds on (most cited first, 30 of 30)

- [Ising formulations of many NP problems](https://mlsanity.com/paper/1302.5843/) (2013)
- [SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence](https://mlsanity.com/paper/2310.16620/) (2023)
- [Efficient Neuromorphic Signal Processing with Loihi 2](https://mlsanity.com/paper/2111.03746/) (2021)
- [A Survey of Neuromorphic Computing and Neural Networks in Hardware](https://mlsanity.com/paper/1705.06963/) (2017)
- [A scalable multi-core architecture with heterogeneous memory structures for Dynamic Neuromorphic Asynchronous Processors (DYNAPs)](https://mlsanity.com/paper/1708.04198/) (2017)
- [Speck: A Smart event-based Vision Sensor with a low latency 327K Neuron Convolutional Neuronal Network Processing Pipeline](https://mlsanity.com/paper/2304.06793/) (2023)
- [Brain-inspired computing: We need a master plan](https://mlsanity.com/paper/2104.14517/) (2021)
- [BindsNET: A machine learning-oriented spiking neural networks library in Python](https://mlsanity.com/paper/1806.01423/) (2018)
- [Low-Power Neuromorphic Hardware for Signal Processing Applications](https://mlsanity.com/paper/1901.03690/) (2019)
- [Spiking Neural Networks for Frame-based and Event-based Single Object Localization](https://mlsanity.com/paper/2206.06506/) (2022)
- [Implementing Spiking Neural Networks on Neuromorphic Architectures: A Review](https://mlsanity.com/paper/2202.08897/) (2022)
- [Sub-mW Neuromorphic SNN audio processing applications with Rockpool and Xylo](https://mlsanity.com/paper/2208.12991/) (2022)
- [Mapping Spiking Neural Networks to Neuromorphic Hardware](https://mlsanity.com/paper/1909.01843/) (2019)
- [Neural Population Coding for Effective Temporal Classification](https://mlsanity.com/paper/1909.08018/) (2019)
- [Sparse Coding by Spiking Neural Networks: Convergence Theory and Computational Results](https://mlsanity.com/paper/1705.05475/) (2017)
- [Neuromorphic Computing is Turing-Complete](https://mlsanity.com/paper/2104.13983/) (2021)
- [Neuromorphic quadratic programming for efficient and scalable model predictive control](https://mlsanity.com/paper/2401.14885/) (2024)
- [Verification and Design Methods for the BrainScaleS Neuromorphic Hardware System](https://mlsanity.com/paper/2003.11455/) (2020)
- [Spiking Analog VLSI Neuron Assemblies as Constraint Satisfaction Problem Solvers](https://mlsanity.com/paper/1511.00540/) (2015)
- [Mapping of Local and Global Synapses on Spiking Neuromorphic Hardware](https://mlsanity.com/paper/1908.08024/) (2019)
- [Solving a steady-state PDE using spiking networks and neuromorphic hardware](https://mlsanity.com/paper/2005.10904/) (2020)
- [Integration of Neuromorphic AI in Event-Driven Distributed Digitized Systems: Concepts and Research Directions](https://mlsanity.com/paper/2210.11190/) (2022)
- [Solving QUBO on the Loihi 2 Neuromorphic Processor](https://mlsanity.com/paper/2408.03076/) (2024)
- [On the computational power and complexity of Spiking Neural Networks](https://mlsanity.com/paper/2001.08439/) (2020)
- [Sampling binary sparse coding QUBO models using a spiking neuromorphic processor](https://mlsanity.com/paper/2306.01940/) (2023)
- [Metaheuristics for (Variable-Size) Mixed Optimization Problems: A Unified Taxonomy and Survey](https://mlsanity.com/paper/2401.03880/) (2024)
- [Rhythmic inhibition allows neural networks to search for maximally consistent states](https://mlsanity.com/paper/1503.02777/) (2015)
- [DFSynthesizer: Dataflow-based Synthesis of Spiking Neural Networks to Neuromorphic Hardware](https://mlsanity.com/paper/2108.02023/) (2021)
- [The effect of heterogeneity on decorrelation mechanisms in spiking neural networks: a neuromorphic-hardware study](https://mlsanity.com/paper/1411.7916/) (2014)
- [Constant-Depth and Subcubic-Size Threshold Circuits for Matrix Multiplication](https://mlsanity.com/paper/2006.14652/) (2020)

## Abstract (arXiv)

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.