CLOCS (Computer with Low Context-Switching Time) Architecture Reference Documents

1988 ◽  
Author(s):  
Mark C. Davis ◽  
Bill O. Gallmeister
Keyword(s):  
Switching Time ◽  
Context Switching

scholarly journals Patterned waveguide liquid crystal displays

RSC Advances ◽  
2020 ◽  
Vol 10 (68) ◽  
pp. 41693-41702
Author(s):  
Yunho Shin ◽  
Jinghua Jiang ◽  
Guangkui Qin ◽  
Qian Wang ◽  
Ziyuan Zhou ◽  
...  
Keyword(s):  
Liquid Crystal ◽  
Switching Time ◽  
Contrast Ratio ◽  
Liquid Crystal Displays ◽  
High Contrast ◽  
High Brightness ◽  
Photo Polymerization ◽  
Ultrafast Switching ◽  
Light Waveguide ◽  
Polymer Stabilized

A polymer stabilized LC based light waveguide display is reported. Performance is improved by patterned photo-polymerization or electrode. It has high brightness, ultrafast switching time, high contrast ratio, and high transmittance for transparent and augmented displays.

scholarly journals Machine Learning for Statistical Modeling

2021 ◽  
Vol 26 (3) ◽  
pp. 1-17
Author(s):  
Urmimala Roy ◽  
Tanmoy Pramanik ◽  
Subhendu Roy ◽  
Avhishek Chatterjee ◽  
Leonard F. Register ◽  
...  
Keyword(s):  
Machine Learning ◽  
Time Distribution ◽  
Switching Time ◽  
A Priori ◽  
Random Access ◽  
Spin Transfer Torque ◽  
Support Vector ◽  
Micromagnetic Simulations ◽  
Physical Systems ◽  
Computational Resources

We propose a methodology to perform process variation-aware device and circuit design using fully physics-based simulations within limited computational resources, without developing a compact model. Machine learning (ML), specifically a support vector regression (SVR) model, has been used. The SVR model has been trained using a dataset of devices simulated a priori, and the accuracy of prediction by the trained SVR model has been demonstrated. To produce a switching time distribution from the trained ML model, we only had to generate the dataset to train and validate the model, which needed ∼500 hours of computation. On the other hand, if 10 6 samples were to be simulated using the same computation resources to generate a switching time distribution from micromagnetic simulations, it would have taken ∼250 days. Spin-transfer-torque random access memory (STTRAM) has been used to demonstrate the method. However, different physical systems may be considered, different ML models can be used for different physical systems and/or different device parameter sets, and similar ends could be achieved by training the ML model using measured device data.

scholarly journals Numerical calculation and experimental study on response characteristics of pneumatic solenoid valves

2019 ◽  
Vol 52 (9-10) ◽  
pp. 1382-1393 ◽  
Author(s):  
Xiang Zhang ◽  
Yonghua Lu ◽  
Yang Li ◽  
Chi Zhang ◽  
Rui Wang
Keyword(s):  
Switching Time ◽  
Response Characteristic ◽  
Test System ◽  
Solenoid Valve ◽  
Circuit Model ◽  
Inlet Pressure ◽  
Driving Voltage ◽  
Response Characteristics ◽  
Aerodynamic Model ◽  
Set Up

In order to analyze the response characteristics of the solenoid valve in depth, the flow field of the solenoid valve is analyzed by means of the computational fluid dynamics, and the aerodynamic parameters that are difficult to be obtained by the traditional methods are obtained with software FLUENT. We also set up the mathematical model of the solenoid valve, including the aerodynamic model, the circuit model, the magnetic circuit model and the mechanical motion model. The calculation is completed in the Simulink, and the results of the calculation are analyzed. A set of the solenoid valve response characteristic test system is built, and the response characteristic parameters such as response time and maximum action frequency of the solenoid valve are tested. The experimental results are verified by comparing them with the simulation results. The final result shows that the response characteristics are basically irrelevant to the action frequency at a suitable working frequency. The open switching time of the solenoid valve decreases with the increase in the inlet pressure and the driving voltage and increases with the increase in the number of coil turns. The close switching time increases with the increase in the inlet pressure, the driving voltage and the number of coil turns.

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