Analyzing the resilience of convolutional neural networks implemented on GPUs: Alexnet as a case study

• Main Authors: Khalid Adam, Ismail Hammad, Izzeldin, I. Mohd, Ibrahim, Younis
• Format: Article
• Language: English
• Published: Faculty of Electrical Engineering, Computer Science and Information Technology, Josip Juraj Strossmayer University of Osijek 2021
• Subjects: TK Electrical engineering. Electronics Nuclear engineering
• Online Access: http://umpir.ump.edu.my/id/eprint/32465/1/Analyzing%20the%20resilience%20of%20convolutional%20neural%20networks%20implemented%20on%20GPUs.pdf; http://umpir.ump.edu.my/id/eprint/32465/; https://doi.org/10.32985/ijeces.12.2.4; https://doi.org/10.32985/ijeces.12.2.4

There have been an extensive use of Convolutional Neural Networks (CNNs) in healthcare applications. Presently, GPUs are the most prominent and dominated DNN accelerators to increase the execution speed of CNN algorithms to improve their performance as well as the Latency. However, GPUs are prone to...