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"

6 / 6 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 Z. Wang Y. Liu Peng Sun Azzedine Boukerche Victor C.M. Leung 31 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 21 7 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 22 3 0 07 Aug 2023
PhiNets: a scalable backbone for low-power AI at the edge Francesco Paissan Alberto Ancilotto Elisabetta Farella 26 31 0 01 Oct 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 86 508 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 948 20,214 0 17 Apr 2017