no code implementations • 2 Oct 2023 • Victor Gao, Issam Hammad, Kamal El-Sankary, Jason Gu
This paper presents a novel method to boost the performance of CNN inference accelerators by utilizing subtractors.
no code implementations • 2 Oct 2023 • Issam Hammad
This paper lists some applications for Canada Deuterium Uranium (CANDU) reactors where such sensors are used and therefore can be impacted by the thermal noise issue if machine learning is utilized.
no code implementations • 26 Feb 2021 • Issam Hammad, Ryan Simpson, Hippolyte Djonon Tsague, Sarah Hall
The proposed CNN model achieves this target by automatically identifying at least a portion of each flaw where further manual analysis is performed to identify the width, the length, and the type of the flaw.
no code implementations • 26 Dec 2019 • Issam Hammad, Kamal El-Sankary, Jason Gu
A comparison of the performance of various machine learning models to predict the direction of a wall following robot is presented in this paper.
no code implementations • 26 Dec 2019 • Issam Hammad, Kamal El-Sankary, Jason Gu
The paper demonstrates that using approximate multipliers for CNN training can significantly enhance the performance in terms of speed, power, and area at the cost of a small negative impact on the achieved accuracy.