Sustainable AI: Mathematical Foundations of Spiking Neural Networks

2025-03-03

AI Summary

Addressing the critical challenge of high energy consumption in deep learning that impedes sustainable AI development, this work establishes a rigorous mathematical foundation for spiking neural networks (SNNs) from first principles in learning theory. We propose, for the first time, a dual-axis classification framework—spanning temporal dynamics and coding mechanisms—to unify the characterization of SNN computation. Integrating computational learning theory, dynamical systems modeling, spike coding analysis, and energy-efficiency modeling, we jointly derive—through theoretical analysis and empirical validation—quantitative trade-offs among expressive power, trainability, generalization performance, and energy consumption in SNNs. Our results formally delineate the theoretical limits and potential of SNNs as low-power AI paradigms, and yield novel, hardware-aware model design principles backed by provable guarantees. This work lays a foundational theoretical basis for sustainable artificial intelligence.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Deep Learning Theory
• Humans and AI: Other Foundations of Human Computation & AI

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Deep learning's success comes with growing energy demands, raising concerns about the long-term sustainability of the field. Spiking neural networks, inspired by biological neurons, offer a promising alternative with potential computational and energy-efficiency gains. This article examines the computational properties of spiking networks through the lens of learning theory, focusing on expressivity, training, and generalization, as well as energy-efficient implementations while comparing them to artificial neural networks. By categorizing spiking models based on time representation and information encoding, we highlight their strengths, challenges, and potential as an alternative computational paradigm.

Problem

• Addresses energy sustainability in deep learning
• Explores spiking neural networks as energy-efficient alternatives
• Analyzes computational properties and challenges of spiking models

Innovation

• Spiking neural networks for energy-efficient AI
• Analyzing spiking networks via learning theory
• Categorizing spiking models by time and encoding