On-Device Intelligence Acceleration Technologies: A Survey

ZHANG Jin-rui; LONG Ting-ting; ZHANG De-yu; XU Yuan; REN Ju; ZHANG Yao-xue

doi:10.12263/DZXB.20240691

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On-Device Intelligence Acceleration Technologies: A Survey

Special Issue: Recipients of CIE Science and Technology Awards | 更新时间：2025-07-24

- On-Device Intelligence Acceleration Technologies: A Survey
- ACTA ELECTRONICA SINICA Vol. 53, Issue 4, Pages: 1063-1102(2025)
- 作者机构：
  
  1.清华大学计算机与科学技术系，北京 100084
  2.中南大学计算机学院，湖南长沙 410083
- 作者简介：
- 基金信息：
  
  National Key Research and Development Program of China(2022YFF0604502);National Natural Science Foundation of China(62122095;62341201)
- DOI：10.12263/DZXB.20240691
  CLC： TP393.0;
- Received：22 July 2024，
  
  Revised：2025-01-15，
  
  Published：25 April 2025
- 稿件说明：
移动端阅览
章晋睿, 龙婷婷, 张德宇, 等. 端智能推理加速技术综述[J]. 电子学报, 2025, 53(04): 1063-1102.

ZHANG Jin-rui, LONG Ting-ting, ZHANG De-yu, et al. On-Device Intelligence Acceleration Technologies: A Survey[J]. Acta Electronica Sinica, 2025, 53(04): 1063-1102.
章晋睿, 龙婷婷, 张德宇, 等. 端智能推理加速技术综述[J]. 电子学报, 2025, 53(04): 1063-1102. DOI：10.12263/DZXB.20240691

ZHANG Jin-rui, LONG Ting-ting, ZHANG De-yu, et al. On-Device Intelligence Acceleration Technologies: A Survey[J]. Acta Electronica Sinica, 2025, 53(04): 1063-1102. DOI：10.12263/DZXB.20240691

摘要

智能下沉是迈向泛在智能时代的必经之路，也推动了端智能（on-device intelligence）技术的飞速发展.通过在终端设备直接部署运行深度学习模型，端智能在实时性、安全性、个性化等方面具有天然优势，已在自动驾驶、卫星侦察、虚拟现实/增强现实（Virtual Reality/Augmented Reality，VR/AR）等众多场景广泛应用.然而，随着深度学习模型参数量不断增大，端侧受限的硬件资源已难以支撑不断增长的计算开销.为提升终端设备在模型推理的计算效率，研究人员从模型算法、编译软件、设备硬件等多个层面开展了系统性优化，有效推动了端智能的发展与演进.本文从算法、软硬件结合优化等方面对现有端侧深度学习模型推理优化工作进行了总结，涵盖模型压缩技术、模型-软件-硬件的协同设计、模型异构并行部署策略以及大模型的端侧优化技术.最后，本文梳理了当前端智能推理加速技术所面临的挑战，并对未来发展趋势进行了展望.

Abstract

Intelligent edge computing is an essential pathway towards the era of pervasive intelligence

and it has propelled the rapid advancement of on-device intelligence technology. By directly deploying and running deep learning models on edge devices

on-device intelligence holds natural advantages in real-time processing

security

and personalization

among other aspects

and has found extensive applications in various scenarios such as autonomous driving

satellite reconnaissance

virtual reality/augmented reality (VR/AR)

and more. However

as the parameters of deep learning models continue to increase

the limited hardware resources at the edge struggle to sustain the growing computational costs. To enhance the computational efficiency of model inference on edge devices

researchers have systematically optimized from multiple perspectives including model algorithms

compilation software

and device hardware

driving the advancement and evolution of on-device intelligence. This paper summarizes existing optimization efforts for deep learning model inference at the edge

covering techniques such as model compression

collaborative design of model-software-hardware

heterogeneous model parallel deployment strategies

and optimizations for large models. Lastly

it outlines the challenges faced by current on-device intelligence inference acceleration technologies and provides insights into future development trends.

关键词

Keywords

references

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