Ultra-high-efficient Writing in Voltage-Control Spintronics Memory(VoCSM); the Most Promising Embedded Memory for Deep Learning

Author(s):  
Y. Ohsawa ◽  
H. Yoda ◽  
N. Shimomura ◽  
S. Shirotori ◽  
S. Fujita ◽  
...  
2018 ◽  
Vol 6 ◽  
pp. 1233-1238
Author(s):  
Y. Ohsawa ◽  
Y. Kato ◽  
T. Inokuchi ◽  
H. Sugiyama ◽  
M. Ishikawa ◽  
...  

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Linfei Yin ◽  
Chenwei Zhang ◽  
Yaoxiong Wang ◽  
Fang Gao ◽  
Jun Yu ◽  
...  

2019 ◽  
Vol 344 ◽  
pp. 499-511 ◽  
Author(s):  
Qingguo Wang ◽  
Geng Zhang ◽  
Chenchen Sun ◽  
Nan Wu

2020 ◽  
Author(s):  
Ahmed abdelreheem ◽  
Ahmed S. A. Mubarak ◽  
Osama A. Omer ◽  
Hamada Esmaiel ◽  
Usama S. Mohamed

Mode selection is normally used in conjunction with Device-to-Device (D2D) millimeter wave (mmWave) communications in 5G networks to overcome the low coverage area, poor reliability and vulnerable to path blocking of mmWave transmissions. Thus, producing a high-efficient D2D mmWave using mode selection based on select the optimal mode with low complexity turns to be a big challenge towards ubiquitous D2D mmWave communications. In this paper, low complexity and high-efficient mode selection in D2D mmWave communications based on deep learning is introduced utilizing the artificial intelligence. In which, deep learning is used to estimate the optimal mode y in the case of blocking of mmWave transmission or low coverage area of mmWave communications. Then, the proposed deep learning model is based on training the model with almost use cases in offline phase to predict the optimal mode for data relaying high-reliability communication in online phase. In mode selection process, the potential D2D transmitter select the mode to transmit the data either based on dedicated D2D communication or through the cellular uplink using the base station (BS) as a relay based on several criteria. The proposed deep learning model is developed to overcome the challenges of selected the optimal mode with low complexity and high efficiency. The simulation analysis show that the proposed mode selection algorithms outperform the conventional techniques in D2D mmWave communication in the spectral efficiency, energy efficiency and coverage probability.


Convolutional neural network (CNN) is actually a deep neural network which plays an important role in image recognition. The CNN recognizes images similar to visual cortex in our eyes. In this proposed work, an accelerator is used for high efficient convolutional computations. The main aim of using the accelerator is to avoid ineffectusal computations and to improve performance and energy efficiency during image recognition without any loss in accuracy. However, the throughput of the accelerator is improved by adding max-pooling function only. Since the CNN includes multiple inputs and intermediate weights for its convolutional computation, the computational complexity is increased enormously. Hence, to reduce the computational complexity of the CNN, a CNN accelerator is proposed in this paper. The accelerator design is simulated and synthesized in Cadence RTL compiler tool with 90nm technology library.


2021 ◽  
Vol 9 (1) ◽  
pp. 659-665
Author(s):  
G. S. Gunanidhi, R. Krishnaveni

Internet of Things (IoT) is the ruling term now-a-days, in which it attracts several smart gadgets and application due to its robust nature and support. In healthcare industry several new technologies are required to improve the stability and provide transparent services to clients. The integration of healthcare maintenance system with respect to Internet of Things support leads to a drastic change in healthcare field as well as this provision provides huge advantages to users. This paper is intended to provide an intense healthcare maintenance scheme by using latest technologies such as Deep Learning, Internet of Things, Fog Computing and Artificial Intelligence. All these innovations are associated together to build a new deep learning strategy called Intense Health Analyzing Scheme (IHAS), in which this proposed approach provides all provisions to clients such as Doctors and Patients with respect to monitor the patient details from anywhere at anytime without any range boundaries. The Fog Computing is an innovative domain, in which it provides ability to the server to operate based on hurdle free processing logic. Artificial Intelligence logic is used to manipulate the health data based on previously trained health records, so that the predictions are more fine compare to the classical healthcare schemes. In traditional schemes it is difficult to raise an alert based on the emergency situation predictions, but in the proposed deep learning strategy assists the proposed approach to send an alert instantly if any emergency cases occurred on patient end. Generally the Fog Servers are used to reduce the occupancy of the storage server and provide reliable storage abilities to server, but in this proposed approach, the fog server is utilized for priority wise data handling nature and stores the health records accordingly. In this nature, the fog servers are handled and provide high efficient results to the clients in an innovative way. With the help of deep learning procedures, the health records are clearly prioritized and maintained into the server end for monitoring. For all this paper introduced a new logic of healthcare maintenance scheme IHAS to provide efficient support to patients as well as doctors in clear manner.


2020 ◽  
Author(s):  
Ahmed abdelreheem ◽  
Ahmed S. A. Mubarak ◽  
Osama A. Omer ◽  
Hamada Esmaiel ◽  
Usama S. Mohamed

Mode selection is normally used in conjunction with Device-to-Device (D2D) millimeter wave (mmWave) communications in 5G networks to overcome the low coverage area, poor reliability and vulnerable to path blocking of mmWave transmissions. Thus, producing a high-efficient D2D mmWave using mode selection based on select the optimal mode with low complexity turns to be a big challenge towards ubiquitous D2D mmWave communications. In this paper, low complexity and high-efficient mode selection in D2D mmWave communications based on deep learning is introduced utilizing the artificial intelligence. In which, deep learning is used to estimate the optimal mode y in the case of blocking of mmWave transmission or low coverage area of mmWave communications. Then, the proposed deep learning model is based on training the model with almost use cases in offline phase to predict the optimal mode for data relaying high-reliability communication in online phase. In mode selection process, the potential D2D transmitter select the mode to transmit the data either based on dedicated D2D communication or through the cellular uplink using the base station (BS) as a relay based on several criteria. The proposed deep learning model is developed to overcome the challenges of selected the optimal mode with low complexity and high efficiency. The simulation analysis show that the proposed mode selection algorithms outperform the conventional techniques in D2D mmWave communication in the spectral efficiency, energy efficiency and coverage probability.


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