Reducing Texture Memory Usage by 2-Channel Color Encoding

GPU Pro 5 ◽  
2014 ◽  
pp. 25-34
Author(s):  
Krzysztof Kluczek
Keyword(s):  
Memory Usage ◽  
Texture Memory

Evidence of syntactic working memory usage in MEG data

2015 ◽  
Author(s):  
Marten van Schijndel ◽  
Brian Murphy ◽  
William Schuler
Keyword(s):  
Working Memory ◽  
Memory Usage

Performance Comparison of Web Backend And Database: A Case Study Of Node.JS, Golang and MySQL, MongoDB

2019 ◽  
Vol 13 ◽  
Author(s):  
Faried Effendy ◽  
Taufik ◽  
Bramantyo Adhilaksono
Keyword(s):  
Response Time ◽  
Open Source ◽  
Poisson Distribution ◽  
Mobile Application ◽  
Web Applications ◽  
Performance Comparison ◽  
Memory Usage ◽  
Data Traffic ◽  
Cpu Utilization

: Substantial research has been conducted to compare web servers or to compare databases, but very limited research combines the two. Node.js and Golang (Go) are popular platforms for both web and mobile application back-ends, whereas MySQL and Go are among the best open source databases with different characters. Using MySQL and MongoDB as databases, this study aims to compare the performance of Go and Node.js as web applications back-end regarding response time, CPU utilization, and memory usage. To simulate the actual web server workload, the flow of data traffic on the server follows the Poisson distribution. The result shows that the combination of Go and MySQL is superior in CPU utilization and memory usage, while the Node.js and MySQL combination is superior in response time.

scholarly journals CMPBUP100006Measuring working memory usage on aphasic individuals and analysis of emotional valence through electroencephalography

2021 ◽  
pp. 100006
Author(s):  
Marcelus Luis Oldoni ◽  
Tiago Catecati ◽  
Eduardo José Legal ◽  
Marcelo Gitirana Gomes Ferreira ◽  
Alejandro Rafael Garcia Ramirez
Keyword(s):  
Working Memory ◽  
Emotional Valence ◽  
Memory Usage

scholarly journals Construction of Apple Leaf Diseases Identification Networks Based on Xception Fused by SE Module

2021 ◽  
Vol 11 (10) ◽  
pp. 4614
Author(s):  
Xiaofei Chao ◽  
Xiao Hu ◽  
Jingze Feng ◽  
Zhao Zhang ◽  
Meili Wang ◽  
...  
Keyword(s):  
Disease Control ◽  
Classification Accuracy ◽  
Mobile Terminal ◽  
Disease Classification ◽  
Memory Usage ◽  
Compression Scheme ◽  
Accurate Identification ◽  
Average Classification Accuracy ◽  
Network Compression ◽  
Control And Management

The fast and accurate identification of apple leaf diseases is beneficial for disease control and management of apple orchards. An improved network for apple leaf disease classification and a lightweight model for mobile terminal usage was designed in this paper. First, we proposed SE-DEEP block to fuse the Squeeze-and-Excitation (SE) module with the Xception network to get the SE_Xception network, where the SE module is inserted between the depth-wise convolution and point-wise convolution of the depth-wise separable convolution layer. Therefore, the feature channels from the lower layers could be directly weighted, which made the model more sensitive to the principal features of the classification task. Second, we designed a lightweight network, named SE_miniXception, by reducing the depth and width of SE_Xception. Experimental results show that the average classification accuracy of SE_Xception is 99.40%, which is 1.99% higher than Xception. The average classification accuracy of SE_miniXception is 97.01%, which is 1.60% and 1.22% higher than MobileNetV1 and ShuffleNet, respectively, while its number of parameters is less than those of MobileNet and ShuffleNet. The minimized network decreases the memory usage and FLOPs, and accelerates the recognition speed from 15 to 7 milliseconds per image. Our proposed SE-DEEP block provides a choice for improving network accuracy and our network compression scheme provides ideas to lightweight existing networks.

scholarly journals Comparing quantum hybrid reinforcement learning to classical methods

2021 ◽  
Author(s):  
Maximilian Moll ◽  
Leonhard Kunczik
Keyword(s):  
Reinforcement Learning ◽  
Quantum Circuits ◽  
Memory Usage ◽  
Computational Power ◽  
Classical Domain ◽  
Q Learning ◽  
Optimal Behavior ◽  
Computational Speed ◽  
Complex Decision ◽  
Hybrid Reinforcement

AbstractIn recent history, reinforcement learning (RL) proved its capability by solving complex decision problems by mastering several games. Increased computational power and the advances in approximation with neural networks (NN) paved the path to RL’s successful applications. Even though RL can tackle more complex problems nowadays, it still relies on computational power and runtime. Quantum computing promises to solve these issues by its capability to encode information and the potential quadratic speedup in runtime. We compare tabular Q-learning and Q-learning using either a quantum or a classical approximation architecture on the frozen lake problem. Furthermore, the three algorithms are analyzed in terms of iterations until convergence to the optimal behavior, memory usage, and runtime. Within the paper, NNs are utilized for approximation in the classical domain, while in the quantum domain variational quantum circuits, as a quantum hybrid approximation method, have been used. Our simulations show that a quantum approximator is beneficial in terms of memory usage and provides a better sample complexity than NNs; however, it still lacks the computational speed to be competitive.

Sign in / Sign up

Export Citation Format

Share Document