TY - JOUR
T1 - Implementation of a dpu-based intelligent thermal imaging hardware accelerator on fpga
AU - Hussein, Abdelrahman S.
AU - Anwar, Ahmed
AU - Fahmy, Yasmine
AU - Mostafa, Hassan
AU - Salama, Khaled N.
AU - Kafafy, Mai
N1 - KAUST Repository Item: Exported on 2022-01-19
Acknowledgements: This work is funded by the Information Technology Industry Development Agency (ITIDA), Information Technology Academia Collaboration (ITAC) Program, Egypt—Grant Number PRP2019.R26.2.
PY - 2021/12/29
Y1 - 2021/12/29
N2 - Thermal imaging has many applications that all leverage from the heat map that can be constructed using this type of imaging. It can be used in Internet of Things (IoT) applications to detect the features of surroundings. In such a case, Deep Neural Networks (DNNs) can be used to carry out many visual analysis tasks which can provide the system with the capacity to make decisions. However, due to their huge computational cost, such networks are recommended to exploit custom hardware platforms to accelerate their inference as well as reduce the overall energy consumption of the system. In this work, an energy adaptive system is proposed, which can intelligently configure itself based on the battery energy level. Besides achieving a maximum speed increase that equals 6.38X, the proposed system achieves significant energy that is reduced by 97.81% compared to a conventional general-purpose CPU.
AB - Thermal imaging has many applications that all leverage from the heat map that can be constructed using this type of imaging. It can be used in Internet of Things (IoT) applications to detect the features of surroundings. In such a case, Deep Neural Networks (DNNs) can be used to carry out many visual analysis tasks which can provide the system with the capacity to make decisions. However, due to their huge computational cost, such networks are recommended to exploit custom hardware platforms to accelerate their inference as well as reduce the overall energy consumption of the system. In this work, an energy adaptive system is proposed, which can intelligently configure itself based on the battery energy level. Besides achieving a maximum speed increase that equals 6.38X, the proposed system achieves significant energy that is reduced by 97.81% compared to a conventional general-purpose CPU.
UR - http://hdl.handle.net/10754/675035
UR - https://www.mdpi.com/2079-9292/11/1/105
UR - http://www.scopus.com/inward/record.url?scp=85121821415&partnerID=8YFLogxK
U2 - 10.3390/electronics11010105
DO - 10.3390/electronics11010105
M3 - Article
SN - 2079-9292
VL - 11
SP - 105
JO - Electronics (Switzerland)
JF - Electronics (Switzerland)
IS - 1
ER -
