Neuromorphic-based metaheuristics: A new generation of low power, low latency and small footprint optimization algorithms
It addresses optimization problems for fields requiring efficient computing, but it is incremental as it adapts existing metaheuristics to a new paradigm.
This paper tackles the problem of implementing optimization algorithms, specifically metaheuristics, using neuromorphic computing as an alternative to traditional Von Neumann architectures, resulting in breakthroughs for low power, low latency, and small footprint solutions.
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.