Enabling Design Methodologies and Future Trends for Edge AI: Specialization and Co-design

TL;DR

This paper surveys recent design methodologies for edge AI, emphasizing specialization and co-design across hardware and software layers to address unique challenges and enable future advancements.

Contribution

It introduces a comprehensive overview of edge AI development methodologies, highlighting the importance of single-layer specialization and cross-layer co-design for future progress.

Findings

Key methodologies include on-device training and specialized hardware design.

Cross-layer co-design offers significant potential for performance improvements.

Identifies future research directions and emerging areas in edge AI.

Abstract

Artificial intelligence (AI) technologies have dramatically advanced in recent years, resulting in revolutionary changes in people's lives. Empowered by edge computing, AI workloads are migrating from centralized cloud architectures to distributed edge systems, introducing a new paradigm called edge AI. While edge AI has the promise of bringing significant increases in autonomy and intelligence into everyday lives through common edge devices, it also raises new challenges, especially for the development of its algorithms and the deployment of its services, which call for novel design methodologies catered to these unique challenges. In this paper, we provide a comprehensive survey of the latest enabling design methodologies that span the entire edge AI development stack. We suggest that the key methodologies for effective edge AI development are single-layer specialization and cross-layer co-design. We discuss representative methodologies in each category in detail, including on-device training methods, specialized software design, dedicated hardware design, benchmarking and design automation, software/hardware co-design, software/compiler co-design, and compiler/hardware co-design. Moreover, we attempt to reveal hidden cross-layer design opportunities that can further boost the solution quality of future edge AI and provide insights into future directions and emerging areas that require increased research focus.