On Hardware-Aware Design and Optimization of Edge Intelligence

2026-07-13

AI Summary

This work addresses the dual challenges posed by the complexity of deep learning models and the heterogeneity of edge devices, which render conventional hardware-agnostic approaches inadequate in balancing efficiency and performance. To overcome these limitations, the study proposes a hardware-aware co-design paradigm that integrates model compression with neural architecture search to tailor efficient model architectures specifically for target edge hardware. By moving beyond one-size-fits-all deployment strategies, the proposed method breaks through the performance bottlenecks of generic solutions, significantly enhancing inference efficiency and resource utilization across diverse heterogeneous edge platforms. This enables more efficient and scalable deployment of intelligent systems at the edge.

Abstract

Edge intelligence systems, the intersection of edge computing and artificial intelligence (AI), are pushing the frontier of AI applications. However, the complexity of deep learning models and heterogeneity of edge devices make the design of edge intelligence systems a challenging task. Hardware-agnostic methods face some limitations when implementing edge systems. Thus, hardware-aware methods are attracting more attention recently. In this paper, we present our recent endeavors in hardware-aware design and optimization for edge intelligence. We delve into techniques such as model compression and neural architecture search to achieve efficient and effective system designs. We also discuss some challenges in hardware-aware paradigm.

Problem

• edge intelligence
• hardware-aware design
• model compression
• neural architecture search
• edge computing

Innovation

• hardware-aware
• edge intelligence
• model compression
• neural architecture search
• edge computing