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

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

25 March 2021

Jinjun Xiong

Luca Benini

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

8 / 8 papers shown

Title
Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey J. H. Liu Yao Du Kun Yang Yan Wang Xiping Hu Zhi Wang Yang Liu Peng Sun Azzedine Boukerche Victor C.M. Leung 43 0 0 03 May 2025
Learning from Students: Applying t-Distributions to Explore Accurate and Efficient Formats for LLMs Jordan Dotzel Yuzong Chen Bahaa Kotb Sushma Prasad Gang Wu Sheng R. Li Mohamed S. Abdelfattah Zhiru Zhang 31 8 0 06 May 2024
FLIQS: One-Shot Mixed-Precision Floating-Point and Integer Quantization Search Jordan Dotzel Gang Wu Andrew Li M. Umar Yun Ni ... Liqun Cheng Martin G. Dixon N. Jouppi Quoc V. Le Sheng R. Li MQ 27 3 0 07 Aug 2023
AI Augmented Edge and Fog Computing: Trends and Challenges Shreshth Tuli Fatemeh Mirhakimi Samodha Pallewatta Syed Zawad G. Casale B. Javadi Feng Yan Rajkumar Buyya N. Jennings 19 56 0 01 Aug 2022
PhiNets: a scalable backbone for low-power AI at the edge Francesco Paissan Alberto Ancilotto Elisabetta Farella 26 32 0 01 Oct 2021
3U-EdgeAI: Ultra-Low Memory Training, Ultra-Low BitwidthQuantization, and Ultra-Low Latency Acceleration Yao Chen Cole Hawkins Kaiqi Zhang Zheng-Wei Zhang Cong Hao 18 8 0 11 May 2021
NetAdapt: Platform-Aware Neural Network Adaptation for Mobile Applications Tien-Ju Yang Andrew G. Howard Bo Chen Xiao Zhang Alec Go Mark Sandler Vivienne Sze Hartwig Adam 90 515 0 09 Apr 2018
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications Andrew G. Howard Menglong Zhu Bo Chen Dmitry Kalenichenko Weijun Wang Tobias Weyand M. Andreetto Hartwig Adam 3DH 950 20,567 0 17 Apr 2017