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2021 International Conference on Computer network Security and Software Engineering, CNSSE 2021

作者： Guo, Xiaozhou Liu, Yi Tan, Kaijun Jin, Min Lu, Huaxiang Institute of Semiconductors Chinese Academy of Sciences Beijing100083 China University of Chinese Academy of Sciences Beijing100089 China Cas Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Beijing200031 China Semiconductor Neural Network Intelligent Perception and Computing Technology Beijing Key Lab Beijing100083 China

Password generation model based on generative adversarial network usually has the problem of high duplicate rate, which further leads to low cover rate. In this regard, we propose PGGAN model. It sets up an additional controller network which is similar to the discriminator in the aspect of structure and function. The discriminator and the controller respectively learn the measure between the distribution of generated password with the real password distribution and the uniform distribution, and then use two measures to teach generator meanwhile. By changing the activation function and loss function of the controller, different measure functions can be selected. The experimental results show that compared with GAN, our PGGAN performs better both in cover rate and duplicate rate. Moreover, Wasserstein distance usually has a better effect to the other measure in model. Specifically, PGGAN with Wasserstein distance can increase the cover rate by 3.57% and reduce the duplicate rate by 30.85% on rockyou dataset. © Published under licence by IOP Publishing Ltd.

关键词： Controllers

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Sdgan: Improve Speech Enhancement Quality by Information Filter 6

Sdgan: Improve Speech Enhancement Quality by Information Fil...

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2021 6th International Symposium on Advances in Electrical, Electronics and Computer Engineering, ISAEECE 2021

作者： Guo, Xiaozhou Liu, Yi Mao, Wenyu Li, Jixing Li, Wenchang Gong, Guoliang Lu, Huaxiang Institute of Semiconductors Chinese Academy of Sciences Beijing100083 China University of Chinese Academy of Sciences Beijing100089 China CAS Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Beijing200031 China Semiconductor Neural Network Intelligent Perception and Computing Technology Beijing Key Lab Beijing100083 China

The speech denoising model based on adversarial generative network has achieved better results than the traditional machine learning model. In this paper, for the short cut connection in the generator, we discuss its influence on the information transfer between encoder and decoder, and propose SDGAN at target. SDGAN sets linear and convolution filters in the short cut connection which adaptively learn the optimal information processing. The information filter still enables the generator to solve the gradient vanishing problem, and it can also avoid information redundancy and improve expression ability. In addition, SDGAN replaces the L1 regularization term in loss function with the L2 regularization term, which not only makes the output speech of the generator closer to the clean speech, but also avoids sparsity. In the experiments, SDGAN significantly performs better than other traditional GAN in five performance metrics (such as PESQ), and the effect of convolution filter is better than that of linear filter. © Published under licence by IOP Publishing Ltd.

关键词： Speech enhancement

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A neural-guided dynamic symbolic network for exploring mathematical expressions from data 24

A neural-guided dynamic symbolic network for exploring mathe...

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Proceedings of the 41st International Conference on Machine Learning

作者： Wenqiang Li Weijun Li Lina Yu Min Wu Linjun Sun Jingyi Liu Yanjie Li Shu Wei Yusong Deng Meilan Hao AnnLab Institute of Semiconductors Chinese Academy of Sciences Beijing China and School of Electronic Electrical and Communication Engineering & School of Integrated Circuits University of Chinese Academy of Sciences Beijing China and Beijing Key Laboratory of Semiconductor Neural Network Intelligent Sensing and Computing Technology Beijing China AnnLab Institute of Semiconductors Chinese Academy of Sciences Beijing China and School of Electronic Electrical and Communication Engineering & School of Integrated Circuits and Beijing Key Laboratory of Semiconductor Neural Network Intelligent Sensing and Computing Technology Beijing China and Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences Beijing China

Symbolic regression (SR) is a powerful technique for discovering the underlying mathematical expressions from observed data. Inspired by the success of deep learning, recent deep generative SR methods have shown promising results. However, these methods face difficulties in processing high-dimensional problems and learning constants due to the large search space, and they don't scale well to unseen problems. In this work, we propose DYSYMNET, a novel neural-guided Dynamic Symbolic network for SR. Instead of searching for expressions within a large search space, we explore symbolic networks with various structures, guided by reinforcement learning, and optimize them to identify expressions that better-fitting the data. Based on extensive numerical experiments on low-dimensional public standard benchmarks and the well-known SRBench with more variables, DYSYMNET shows clear superiority over several representative baseline models. Open source code is available at https://***/AILWQ/DySymNet.

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Research on Convolution Decomposition and Hardware Acceleration based on FPGA

Research on Convolution Decomposition and Hardware Accelerat...

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International Conference on Computer Engineering and Applications (ICCEA)

作者： Jinzhong He Weijun Li Jian Xu Ming Zhang Huiqu Yang Institute of Semiconductors Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Beijing Key Laboratory of Semiconductor Neural Network Intelligent Sensing and Computing Technology Beijing China DapuStor Corporation Shenzhen China

Convolutional neural network (CNN) is an artificial intelligence algorithm with a wide application foundation. In recent years, to improve the computational speed of the computation-intensive CNN models, many researchers have accelerated hardware implementation on FPGA. However, the acceleration algorithms are mostly unfriendly to hardware or have poor flexibility, which leads to a decrease in computing power and an increase in data access. In this paper, we propose a convolution decomposition method in the time domain, which fully considers the behavior of convolution operation and the difference between data streams in hardware. Besides, we design a hardware acceleration architecture. The results show that this design can be compatible with convolution and full connection operations of various sizes and strides. Hardware tailoring and cascade expansion are proposed for different application scenarios. Experiments show that the computing performance per DSP is 0.39 GOP/s, which demonstrates that the hardware design has a better multiplier utilization than similar works.

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Point convolutional neural network algorithm for Ising model ground state research based on spring vibration

arXiv

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arXiv 2023年

作者： Jiang, Zhelong Chen, Gang Qiao, Ruixiu Feng, Pengcheng Chen, Yihao Su, Junjia Zhao, Zhiyuan Jin, Min Chen, Xu Li, Zhigang Lu, Huaxiang Institute of Semiconductors Chinese Academy of Sciences Beijing China Materials and Optoelectronics Research Center University of Chinese Academy of Sciences Beijing China School of Microelectronics University of Science and Technology of China Hefei China College of Microelectronics University of Chinese Academy of Sciences Beijing China Semiconductor Neural Network Intelligent Perception and Computing Technology Beijing Key Laboratory Beijing China

The ground state search of the Ising model can be used to solve many combinatorial optimization problems. Under the current computer architecture, an Ising ground state search algorithm suitable for hardware computing is necessary for solving practical problems. Inspired by the potential energy conversion of springs, we propose a point convolutional neural network algorithm for ground state search based on spring vibration model, called Spring-Ising Algorithm. Spring-Ising Algorithm regards the spin as a moving mass point connected to a spring and establish the equation of motion for all spins. Spring-Ising Algorithm can be mapped on the GPU or AI chips through the basic structure of the neural network for fast and efficient parallel computing. The algorithm has very productive results for solving the Ising model and has been test in the recognized test benchmark K2000. The algorithm introduces the concept of dynamic equilibrium to achieve a more detailed local search by dynamically adjusting the weight of the Ising model in the spring oscillation model. Finally, there is the simple hardware test speed evaluation. Spring-Ising Algorithm can provide the possibility to calculate the Ising model on a chip which focuses on accelerating neural network calculations. © 2023, CC BY.

关键词： Equations of motion

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Fitting objects with implicit polynomials by deep neural network

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Optoelectronics Letters 2023年第1期19卷 60-64页

作者： LIU Jingyi YU Lina SUN Linjun TONG Yuerong WU Min LI Weijun Institute of Semiconductors Chinese Academy of SciencesBeijing100083China Beijing Key Laboratory of Semiconductor Neural Network Intelligent Sensing and Computing Technology Beijing100083China School of Integrated Circuits University of Chinese Academy of SciencesBeijing100049China Shenzhen DAPU Microelectronics Co. Ltd.Shenzhen518116China

Implicit polynomials(IPs)are considered as a powerful tool for object curve fitting tasks due to their simplicity and fewer *** traditional linear methods,such as 3L,Min Var,and Min Max,often achieve good performances in fitting simple objects,but usually work poorly or even fail to obtain closed curves of complex object *** handle the complex fitting issues,taking the advantages of deep neural networks,we designed a neural network model continuity-sparsity constrained network(CSC-Net)with encoder and decoder structure to learn the coefficients of ***,the continuity constraint is added to ensure the obtained curves are closed,and the sparseness constraint is added to reduce the spurious zero sets of the fitted *** experimental results show that better performances have been obtained on both simple and complex object fitting tasks.

关键词： continuity constraint polynomials

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Deep Learning-based 3D Point Cloud Classification: A Systematic Survey and Outlook

arXiv

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arXiv 2023年

作者： Zhang, Huang Wang, Changshuo Tian, Shengwei Lu, Baoli Zhang, Liping Ning, Xin Bai, Xiao The School of Software Xinjiang University Xinjiang830000 China The Institute of Semiconductors Chinese Academy of Sciences Beijing100083 China Center of Materials Science and Optoelectronics Engineering School of Microelectronics Beijing Key Laboratory of Semiconductor Neural Network Intelligent Sensing and Computing Technology Beijing100083 China University of Chinese Academy of Sciences Beijing100049 China Cognitive Computing Technology Joint Laboratory Wave Group Beijing102208 China Beijing Key Laboratory of Semiconductor Neural Network Intelligent Sensing and Computing Technology Beijing100083 China The School of Computer Science and Engineering Beihang University Beijing100083 China

In recent years, point cloud representation has become one of the research hotspots in the field of computer vision, and has been widely used in many fields, such as autonomous driving, virtual reality, robotics, etc. Although deep learning techniques have achieved great success in processing regular structured 2D grid image data, there are still great challenges in processing irregular, unstructured point cloud data. Point cloud classification is the basis of point cloud analysis, and many deep learning-based methods have been widely used in this task. Therefore, the purpose of this paper is to provide researchers in this field with the latest research progress and future trends. First, we introduce point cloud acquisition, characteristics, and challenges. Second, we review 3D data representations, storage formats, and commonly used datasets for point cloud classification. We then summarize deep learning-based methods for point cloud classification and complement recent research work. Next, we compare and analyze the performance of the main methods. Finally, we discuss some challenges and future directions for point cloud classification. Copyright © 2023, The Authors. All rights reserved.

关键词： Digital storage

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SSCGAN: Speech style conversion based on GAN

SSCGAN: Speech style conversion based on GAN

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2022 International Conference on Algorithms, Microchips and network Applications

作者： Li, Jixing Guo, Xiaozhou Shen, Ronxuan Lu, Huaxiang Wang, Xinggang Cao, Zhanzhong Zhang, Chi Mao, Wenyu Institute of Semiconductors Chinese Academy of Sciences Beijing100083 China University of Chinese Academy of Sciences Beijing100089 China Materials and Optoelectronics Research Center University of Chinese Academy of Sciences Beijing100083 China College of Microelectronics University of Chinese Academy of Sciences Beijing China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai200031 China Semiconductor Neural Network Intelligent Perception and Computing Technology Beijing Key Laboratory Beijing100083 China Nanjing Research Institute of Information Technology Nanjing210009 China

ISBN: (纸本)9781510653290

Speech conversion has significant applications in medical, robotics, and other industries. With the rise of deep learning, CycleGAN is widely used in speech conversion technology. However, the existing CycleGAN-based methods do not consider the speech signal's temporal and spatial features. In addition, the training of CycleGAN is difficult to converge due to the gradient disappearance problem of the generator. We propose SSCGAN, whose generator is a U-shaped encoder-decoder network that extracts the temporal and spatial features by using 1DCNN and 2DCNN in parallel. A feature fusion module based on multi-scale mixed convolution is embedded between encoder and decoder to achieve high-level fusion of spatial features and temporal features. To make the network training more stable and easier to converge, SSCGAN uses Wasserstein distance instead of the original Jensen-Shannon divergence to calculate the distance of the probability distribution, which can alleviate the gradient extinction problem for generators. In addition, SSCGAN utilizes the PatchGAN structure in the discriminator, which considers the samples' local details by dividing them into different patches. It can improve the discriminative ability of SSCGAN. The experiment results in the nonparallel corpus database VCC 2018 show that SSCGAN is superior to existing methods such as CycleGAN-VC, StarGan-VC. In inter-gender speech conversion, the MSD of SSCGAN is decreased by 0.162 on average compared to other methods, and in intra-gender speech conversion, the MSD is decreased by 0.118 on average. In subjective evaluation, participants also think SSCGAN is the best. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Generative adversarial networks

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PruneSymNet: A Symbolic neural network and Pruning Algorithm for Symbolic Regression

arXiv

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arXiv 2024年

作者： Wu, Min Li, Weijun Yu, Lina Li, Wenqiang Liu, Jingyi Li, Yanjie Hao, Meilan AnnLab Institute of Semiconductors Chinese Academy of Sciences Beijing100083 China Center of Materials Science and Optoelectronics Engineering School of Microelectronics University of Chinese Academy of Sciences Beijing100049 China Beijing Key Laboratory of Semiconductor Neural Network Intelligent Sensing and Computing Technology Beijing100083 China

Symbolic regression aims to derive interpretable symbolic expressions from data in order to better understand and interpret data. In this study, a symbolic network called PruneSymNet is proposed for symbolic regression. This is a novel neural network whose activation function consists of common elementary functions and operators. The whole network is differentiable and can be trained by gradient descent method. Each subnetwork in the network corresponds to an expression, and our goal is to extract such subnetworks to get the desired symbolic expression. Therefore, a greedy pruning algorithm is proposed to prune the network into a subnetwork while ensuring the accuracy of data fitting. The proposed greedy pruning algorithm preserves the edge with the least loss in each pruning, but greedy algorithm often can not get the optimal solution. In order to alleviate this problem, we combine beam search during pruning to obtain multiple candidate expressions each time, and finally select the expression with the smallest loss as the final result. It was tested on the public data set and compared with the current popular algorithms. The results showed that the proposed algorithm had better accuracy. Copyright © 2024, The Authors. All rights reserved.

关键词： Gradient methods

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Incorporating Higher-Knowledge into Deep Symbolic Regression Under Generative neural network Framework

SSRN

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SSRN 2024年

作者： Liu, Jingyi Li, Weijun Yu, Lina Wu, Min Li, Wenqiang Li, Yanjie Hao, Meilan Annlab Institute of Semiconductors Chinese Academy of Sciences Beijing100083 China Center of Materials Science and Optoelectronics Engineering School of Integrated Circuits University of Chinese Academy of Sciences Beijing100049 China Beijing Key Laboratory of Semiconductor Neural Network Intelligent Sensing and Computing Technology Beijing100083 China

This paper introduces MSR, a generative neural network-based framework that incorporates an automatic module learning feature, aiming to enhance the search accuracy of Symbolic Regression (SR). Unlike existing deep model-based SR approaches, MSR identifies valuable sub-structures named modules, which are utilized to construct the final solution. Modules are considered high-order knowledge and act as fundamental operators, expanding the search library of SR. The proposed algorithm enables self-learning or self-evolution of modules as part of the learning component. By incorporating variables or partial solutions as input arguments, MSR facilitates the formation of sophisticated, higher-order, and constructive sub-structures, thereby improving search accuracy. Specifically, we propose a module extraction strategy that generates modules hierarchically from the expression tree, along with a module update mechanism designed to effectively eliminate unnecessary modules while incorporating new useful ones. To validate the effectiveness of MSR, we conducted experiments on 60 SR problems. Through ablation studies, we analyze the impact of module utilization and the module update mechanism. The experimental results demonstrate that our method outperforms scenarios where modules are not utilized or random updates are applied. Furthermore, our approach exhibits higher accuracy in SR problems involving constants, surpassing some state-of-the-arts. Our code has been released at https://***/r/MSR. © 2024, The Authors. All rights reserved.

关键词： Extraction

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