Enabling Design Methodologies and Future Trends for Edge AI: Specialization and Codesign

Cong Hao, Jordan Dotzel, Jinjun Xiong, Luca Benini, Zhiru Zhang, Deming Chen

Research output: Contribution to journalReview articlepeer-review


Significant growth is seen in artificial intelligence (AI), in particular deep learning (DL), which has made remarkable progress in various areas such as computer vision, natural language processing, health care, autonomous driving, and surveillance. To accomplish this, AI technologies have broadened from a centralized fashion to mobile or distributed fashion, opening a new era called edge AI, with dramatic advancements that are substantially changing everyday technology, social behavior, and lifestyles. Edge AI couples intelligence and analysis to a broad collection of connected devices and systems for data collection, caching, and processing. It enables a wide variety of new promising applications where data collection and analysis are combined. Billions of mobile users are exploiting various smartphone applications such as translation services, digital assistants, and health monitoring services.

Original languageEnglish (US)
Article number9391712
Pages (from-to)7-26
Number of pages20
JournalIEEE Design and Test
Issue number4
StatePublished - Aug 2021


  • Edge AI
  • IoT
  • co-design
  • design methodology
  • edge device
  • machine learning

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Electrical and Electronic Engineering


Dive into the research topics of 'Enabling Design Methodologies and Future Trends for Edge AI: Specialization and Codesign'. Together they form a unique fingerprint.

Cite this