用类脑计算实现低功耗优化算法，突破传统架构瓶颈
Neuromorphic-based metaheuristics: A new generation of low power, low latency and small footprint optimization algorithms
- 将类脑计算与元启发式算法结合，设计新型低功耗优化方法
- 类脑优化算法在功耗、延迟和体积上均显著优于传统架构
- 适合边缘设备部署，推动智能系统轻量化发展
类脑计算（NC）引入了一种全新的算法范式，彻底改变传统冯·诺依曼架构的数字计算方式。NC通过脉冲神经网络（SNNs）模拟大脑的神经动力学特性。目前大部分研究聚焦于机器学习应用和神经科学模拟。本文探索了基于类脑计算范式的优化算法建模与实现，特别是元启发式算法，为解决优化问题带来突破性进展。这类基于类脑计算的元启发式算法（Nheuristics）具有低功耗、低延迟和小体积的特点。由于类脑系统与传统冯·诺依曼计算机存在本质差异，Nheuristics的设计与实现面临诸多挑战。本文基于分类与关键分析，梳理了不同家族的元启发式算法及其适用的优化问题。同时讨论了未来需解决的方向，以推动Nheuristics的进一步发展与应用。
原文摘要 · Abstract (English)
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.
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