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 Jing Liu Yao Du Kun Yang Yan Wang Xiping Hu Zehua 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 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 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 21 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