Results 251 to 260 of about 41,575 (296)
Some of the next articles are maybe not open access.
Are Mobile DNN Accelerators Accelerating DNNs?
Proceedings of the 5th International Workshop on Embedded and Mobile Deep Learning, 2021Deep neural networks (DNNs) are running on many mobile and embedded devices with the goal of energy efficiency and highest possible performance. However, DNN workloads are getting more computationally intensive, and simultaneously their deployment is ever-increasing.
Qingqing Cao +4 more
openaire +1 more source
IEEE Transactions on Mobile Computing, 2023 Mobile Edge Computing (MEC) has emerged as a promising paradigm catering to overwhelming explosions of mobile applications, by offloading compute-intensive tasks to MEC networks for processing.
Weifa Liang, Yuchen Li, Zichuan Xu
exaly +2 more sources
Vertical federated DNN training
Physical Communication, 2021Abstract In the training process of distributed machine learning, the data possessed at distinct companies usually contain different features. Labels may even be lacked at certain companies. Therefore, one option is that multiple companies perform joint training in the form of federated learning (FL).
Mingjun Dai +4 more
openaire +1 more source
DyHard-DNN: Even More DNN Acceleration with Dynamic Hardware Reconfiguration
2018 55th ACM/ESDA/IEEE Design Automation Conference (DAC), 2018Deep Neural Networks (DNNs) have demonstrated their utility across a wide range of input data types, usable across diverse computing substrates, from edge devices to datacenters. This broad utility has resulted in myriad hardware accelerator architectures.
Mateja Putic +5 more
openaire +1 more source
Proceedings of the 2019 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays, 2019
The efficacy and effectiveness of Convolutional Neural Networks (CNNs) have been proven in a wide range of machine learning applications. However, the high computational complexity of CNNs presents a critical challenge towards their broader adoption in real-time and power-efficient scenarios.
Yao Chen 0008 +4 more
openaire +1 more source
The efficacy and effectiveness of Convolutional Neural Networks (CNNs) have been proven in a wide range of machine learning applications. However, the high computational complexity of CNNs presents a critical challenge towards their broader adoption in real-time and power-efficient scenarios.
Yao Chen 0008 +4 more
openaire +1 more source
2021
This chapter starts by describing the main challenges that face deep learning systems. It then explores the fundamentals involved in the training of DNNs, and concludes with some examples of using MindSpore to implement DNNs.
openaire +1 more source
This chapter starts by describing the main challenges that face deep learning systems. It then explores the fundamentals involved in the training of DNNs, and concludes with some examples of using MindSpore to implement DNNs.
openaire +1 more source
P‐DNN: Parallel DNN based IDS framework for the detection of IoT vulnerabilities
SECURITY AND PRIVACY, 2023SummaryThe rapid growth of the Internet of Things (IoT) in our daily life has recently received attention from hackers in releasing novel attacks. This is because the existing traditional Intrusion Detection System (IDS) uses an alert‐based approach that cannot detect new emerging attacks, making it unfeasible for devices with limited resources.
B. S. Sharmila, Rohini Nagapadma
openaire +1 more source
DAPP: Accelerating Training of DNN
2018 IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS), 2018Deep Neural Networks (DNNs) are one of the leading classification algorithms and have achieved big milestones such as GoogleNet and AlphaGo. Training of neural network is a time-consuming affair and is proportional to the depth of network and number of computations carried out in each layer.
Sapna Sapna +2 more
openaire +1 more source
2019 IEEE International Symposium on Information Theory (ISIT), 2019
A modified sphere decoding (SD) scheme is proposed for multiple-input multiple-output (MIMO) communication systems in this paper. The contribution of the paper includes the introduction of a systematic approach to sphere radius design and control based on Deep Neural Networks (DNNs) as well as the complexity advantage yielded by the proposed scheme ...
Aymen Askri, Ghaya Rekaya-Ben Othman
openaire +1 more source
A modified sphere decoding (SD) scheme is proposed for multiple-input multiple-output (MIMO) communication systems in this paper. The contribution of the paper includes the introduction of a systematic approach to sphere radius design and control based on Deep Neural Networks (DNNs) as well as the complexity advantage yielded by the proposed scheme ...
Aymen Askri, Ghaya Rekaya-Ben Othman
openaire +1 more source
AutoMarkov DNNs for object classification
2016 23rd International Conference on Pattern Recognition (ICPR), 2016Recent advances in the area of Deep Convolutional Neural Networks have led to steady progress, mainly observed in the field of object classification and localization. Extensive testing helped generate frameworks guaranteeing the initiation of successful network architectures.
Cosmin Toca, Carmen Patrascu, Mihai Ciuc
openaire +1 more source