SRGC-Nets: Sparse Repeated Group Convolutional Neural Networks

Lu, Yao; Lu, Guangming; Lin, Rui; Li, Jinxing; Zhang, David

doi:10.1109/tnnls.2019.2933665

Cited by 29 publications

(12 citation statements)

References 27 publications

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“…By comparing the predicted labels with the true labels, the performance of the shallow learning algorithms can be achieved. The most widely used algorithms include SVM, DT, NB, and Kmeans [39,40]. Buczak et al [39] provided a summary as a survey to describe some machine learning and data mining methods, such as DT, SVM, RF, and NB, which were used for cybersecurity intrusion detection.…”

Section: Related Workmentioning

confidence: 99%

“…The most widely used algorithms include SVM, DT, NB, and Kmeans [39,40]. Buczak et al [39] provided a summary as a survey to describe some machine learning and data mining methods, such as DT, SVM, RF, and NB, which were used for cybersecurity intrusion detection. Kruczkowski and Szynkiewicz [41] used SVM with kernels to build a malware detection model.…”

Section: Related Workmentioning

confidence: 99%

“…Without increasing the complexity of the parameters, the accuracy of the model could be improved, and the number of super parameters could be reduced. Lu et al [39] proposed a novel repeated group convolutional kernel (RGC) to remove the filter's redundancy from group extent. SRGC-Nets worked well in not only reducing the model size and computational complexity, but also decreasing the testing and training running time.…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

“…At the same time, by the convolution operation and pooling operation, a CNN can achieve the purpose of reducing the dimensions of the input data [57]. Additionally, grouped convolution kernels and pooling filters can reduce the number of parameters and improve the performance [39]. Therefore, we use 1D group convolution kernels to build a 1D group CNN model in this work.…”

Section: Group Convolutional Neural Network For Featurementioning

confidence: 99%

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Network Threat Detection Based on Group CNN for Privacy Protection

Zhang

et al. 2021

Wireless Communications and Mobile Computing

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The Internet of Things (IoT) contains a large amount of data, which attracts various types of network attacks that lead to privacy leaks. With the upgrading of network attacks and the increase in network security data, traditional machine learning methods are no longer suitable for network threat detection. At the same time, data analysis techniques and deep learning algorithms have developed rapidly and have been successfully applied to a variety of tasks for privacy protection. Convolutional neural networks (CNNs) are typical deep learning models that can learn and reconstruct features accurately and efficiently. Therefore, in this paper, we propose a group CNN models that is based on feature correlations to learn features and reconstruct security data. First, feature correlation coefficients are computed to measure the relationships among the features. Then, we sort the correlation coefficients in descending order and group the data by columns. Second, a 1D group CNN model with multiple 1D convolution kernels and 1D pooling filters is built to address the grouped data for feature learning and reconstruction. Third, the reconstructed features are input to shadow machine learning models for network threat prediction. The experimental results show that features reconstructed by the group CNN can reduce the dimensions and achieve the best performance compared to the other present dimension reduction algorithms. At the same time, the group CNN can decrease the floating point of operations (FLOP), parameters, and running time compared to the basic 1D CNN.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

Section: Group Convolutional Neural Network For Featurementioning

confidence: 99%

See 2 more Smart Citations

Network Threat Detection Based on Group CNN for Privacy Protection

Zhang

et al. 2021

Wireless Communications and Mobile Computing

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“…FC layers usually contain a large number of neurons, which are fully connected with previous layers through weights. Figure 2 shows a single FC layer architecture [7]. The huge number of weights involved in many modern CNN architectures makes their implementation on a general purpose processor highly challenging due to the high memory and bandwidth requirements [4,7,8].…”

Section: Introductionmentioning

confidence: 99%

Sensitive, Linear, Robust Current-To-Time Converter Circuit for Vehicle Automation Application

et al. 2020

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Voltage-to-time and current-to-time converters have been used in many recent works as a voltage-to-digital converter for artificial intelligence applications. In general, most of the previous designs use the current-starved technique or a capacitor-based delay unit, which is non-linear, expensive, and requires a large area. In this paper, we propose a highly linear current-to-digital converter. An optimization method is also proposed to generate the optimal converter design containing the smallest number of PMOS and sensitive circuits such as a differential amplifier. This enabled our design to be more stable and robust toward negative bias temperature instability (NBTI) and process variation. The proposed converter circuit implements the point-wise conversion from current-to-time, and it can be used directly for a variety of applications, such as analog-to-digital converters (ADC), used in built-in computational random access (C-RAM) memory. The conversion gain of the proposed circuit is 3.86 ms/A, which is 52 times greater than the conversion gains of state-of-the-art designs. Further, various time-to-digital converter (TDC) circuits are reviewed for the proposed current-to-time converter, and we recommend one circuit for a complete ADC design.

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Intrinsic Decay Property of Ti/TiO_x/Pt Memristor for Reinforcement Learning

Dai

Guo

Feng

et al. 2023

Advanced Intelligent Systems

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A memristor‐based reinforcement learning (RL) system has shown outstanding performance in achieving efficient autonomous decision‐making and edge computing. Sarsa (λ) is a classical multistep RL algorithm that records state with λ decay and guides policy updates, significantly improving the algorithm convergence speed. However, λ decay implementation of traditional computing hardware is confined by the extensive computation of power exponential decay. Herein, the value update equation for Sarsa (λ) is implemented by using the topological structure of the memristor array, without complex circuits. Where, most importantly, the critical λ decay function is realized by a TiOx‐based memristor with conductance decay property. The energy required for floating‐point operations can be significantly reduced while accelerating the convergence speed. Then, a path planning task is demonstrated based on intrinsic conductance decay property and shows outstanding performance. Finally, the information of rounds used for the task is obtained, which is based on the intrinsic decay property of the TiOx‐based memristor, maps into a 32 × 32 memristor array in parallel to calculate the value of each round. The results indicate that the experimental data have similar results to the simulations. Herein, thus, it provides a hardware‐enabled scheme for the memristor‐based RL algorithm implementation.

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SRGC-Nets: Sparse Repeated Group Convolutional Neural Networks

Cited by 29 publications

References 27 publications

Network Threat Detection Based on Group CNN for Privacy Protection

Network Threat Detection Based on Group CNN for Privacy Protection

Sensitive, Linear, Robust Current-To-Time Converter Circuit for Vehicle Automation Application

Intrinsic Decay Property of Ti/TiO_x/Pt Memristor for Reinforcement Learning

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SRGC-Nets: Sparse Repeated Group Convolutional Neural Networks

Cited by 29 publications

References 27 publications

Network Threat Detection Based on Group CNN for Privacy Protection

Network Threat Detection Based on Group CNN for Privacy Protection

Sensitive, Linear, Robust Current-To-Time Converter Circuit for Vehicle Automation Application

Intrinsic Decay Property of Ti/TiOx/Pt Memristor for Reinforcement Learning

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Product

Resources

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Intrinsic Decay Property of Ti/TiO_x/Pt Memristor for Reinforcement Learning