Performance/power assessment of CNN packages on embedded automotive platforms

12 October 2023

Papers citing "Performance/power assessment of CNN packages on embedded automotive platforms"

6 / 6 papers shown

Title
What is the State of Neural Network Pruning? Davis W. Blalock Jose Javier Gonzalez Ortiz Jonathan Frankle John Guttag 286 1,054 0 06 Mar 2020
MobileNetV2: Inverted Residuals and Linear Bottlenecks Mark Sandler Andrew G. Howard Menglong Zhu A. Zhmoginov Liang-Chieh Chen 234 19,353 0 13 Jan 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 1.2K 20,900 0 17 Apr 2017
YOLO9000: Better, Faster, Stronger Joseph Redmon Ali Farhadi VLM ObjD 183 15,641 0 25 Dec 2016
SSD: Single Shot MultiBox Detector Wen Liu Dragomir Anguelov D. Erhan Christian Szegedy Scott E. Reed Cheng-Yang Fu Alexander C. Berg ObjD BDL 259 29,915 0 08 Dec 2015
Caffe: Convolutional Architecture for Fast Feature Embedding Yangqing Jia Evan Shelhamer Jeff Donahue Sergey Karayev Jonathan Long Ross B. Girshick S. Guadarrama Trevor Darrell VLM BDL 3DV 296 14,717 0 20 Jun 2014