Multi-Complexity-Loss DNAS for Energy-Efficient and Memory-Constrained
Deep Neural Networks

Multi-Complexity-Loss DNAS for Energy-Efficient and Memory-Constrained Deep Neural Networks

1 June 2022

Alessio Burrello

Luca Benini

Daniele Jahier Pagliari

Papers citing "Multi-Complexity-Loss DNAS for Energy-Efficient and Memory-Constrained Deep Neural Networks"

5 / 5 papers shown

Title
Enhancing Neural Architecture Search with Multiple Hardware Constraints for Deep Learning Model Deployment on Tiny IoT Devices Alessio Burrello Matteo Risso Beatrice Alessandra Motetti Enrico Macii Luca Benini Daniele Jahier Pagliari 26 8 0 11 Oct 2023
PLiNIO: A User-Friendly Library of Gradient-based Methods for Complexity-aware DNN Optimization Daniele Jahier Pagliari Matteo Risso Beatrice Alessandra Motetti Alessio Burrello 21 8 0 18 Jul 2023
Benchmarking TinyML Systems: Challenges and Direction Colby R. Banbury Vijay Janapa Reddi Max Lam William Fu A. Fazel ... Jae-sun Seo Jeff Sieracki Urmish Thakker Marian Verhelst Poonam Yadav 109 228 0 10 Mar 2020
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
Neural Architecture Search with Reinforcement Learning Barret Zoph Quoc V. Le 271 5,329 0 05 Nov 2016