Onboard Optimization and Learning: A Survey

TL;DR

This survey reviews recent advances in onboard learning for edge AI, focusing on methods that optimize model efficiency, enhance inference speed, and ensure privacy on resource-limited devices.

Contribution

It provides a comprehensive overview of techniques addressing computational, security, and scalability challenges in onboard learning for edge AI.

Findings

Model compression improves efficiency and reduces inference time.

Decentralized learning enhances scalability and privacy.

Hardware-software co-design accelerates onboard AI deployment.

Abstract

Onboard learning is a transformative approach in edge AI, enabling real-time data processing, decision-making, and adaptive model training directly on resource-constrained devices without relying on centralized servers. This paradigm is crucial for applications demanding low latency, enhanced privacy, and energy efficiency. However, onboard learning faces challenges such as limited computational resources, high inference costs, and security vulnerabilities. This survey explores a comprehensive range of methodologies that address these challenges, focusing on techniques that optimize model efficiency, accelerate inference, and support collaborative learning across distributed devices. Approaches for reducing model complexity, improving inference speed, and ensuring privacy-preserving computation are examined alongside emerging strategies that enhance scalability and adaptability in dynamic environments. By bridging advancements in hardware-software co-design, model compression, and decentralized learning, this survey provides insights into the current state of onboard learning to enable robust, efficient, and secure AI deployment at the edge.