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High Performance Big Data and intelligent Systems (HPBD&IS)

作者： Ang Wang Lina Yu Yuyan Lan Weixin Zhou Wan’Ang Xiao Chinese Academy of Sciences Institute of Semiconductors Beijing China School of Microelectronics University of Chinese Academy of Science Beijing China Beijing Key Laboratory of Semiconductor Neural Network Intelligent Sensing and Computing Technology Beijing China

ISBN: (纸本)9781665413282

In order to reduce noise and power consumption of audio electronic products, audio noise reduction and its low- power hardware implementation is studied. Considering the noise reduction effect and the amount of calculation, Log-MMSE algorithm and statistical-model-based Voice Activity Detection (VAD) noise estimator is selected in this paper. And the amount of calculation is optimized at the algorithm level. The ability of tracking non-stable noise is simulated and studied, and a possible noise reduction failure in hardware applications is prevented. In the hardware design, the existing memory is reused to save an extra memory. Two special operations are combined with one operation, and approximate calculations are implemented according to the characteristics of the combined operation. The design of this paper has passed the functional verification on FPGA and taped out with a digital hearing aids chip under the SMIC 0.13μm Mixed Signal Process. The area and the post-simulation power consumption under the tt process corner are 0.206mm 2 and 15.3μW respectively, which meet the requirements of low-power audio chip.

关键词： Voice activity detection Power demand Noise reduction Signal processing algorithms Speech enhancement Approximation algorithms Hearing aids

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Search-based Ordered Password Generation of Autoregressive neural networks

arXiv

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arXiv 2024年

作者： Jin, Min Ye, Junbin Shen, Rongxuan Lu, Huaxing 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 Beijing100049 China Beijing Key Lab of Semiconductor Neural Network Intelligent Perception and Computing Technology Beijing100083 China

Passwords are the most widely used method of authentication and password guessing is the essential part of password cracking and password security research. The progress of deep learning technology provides a promising way to improve the efficiency of password guessing. However, current research on neural network password guessing methods mostly focuses on model structure and has overlooked the generation method. Due to the randomness of sampling, not only the generated passwords have a large number of duplicates, but also the order in which passwords generated is random, leading to inefficient password attacks. In this paper, we propose SOPG, a search-based ordered password generation method, which enables the password guessing model based on autoregressive neural network to generate passwords in approximately descending order of probability. Experiment on comparison of SOPG and Random sampling shows passwords generated by SOPG do not repeat, and when they reach the same cover rate, SOPG requires fewer inferences and far fewer generated passwords than Random sampling, which brings great efficiency improvement to subsequent password attacks. We build SOPGesGPT, a password guessing model based on GPT, using SOPG to generate passwords. Compared with the most influential models OMEN, FLA, PassGAN, VAEPass and the latest model PassGPT in one-site test, experiments show that SOPGesGPT is far ahead in terms of both effective rate and cover rate. As to cover rate that everyone recognizes, SOPGesGPT reaches 35.06%, which is 254%, 298%, 421%, 380%, 81% higher than OMEN, FLA, PassGAN, VAEPass, and PassGPT respectively. © 2024, CC BY.

关键词： Authentication

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A Local Descriptor with Physiological Characteristic for Finger Vein Recognition

A Local Descriptor with Physiological Characteristic for Fin...

引用

International Conference on Pattern Recognition

作者： Liping Zhang Weijun Li Xin Ning Linjun Sun Xiaoli Dong Institute of Semiconductors Chinese Academy of Sciences Beijing China Beijing Key Laboratory of Semiconductor Neural Network Intelligent Sensing and Computing Technology Beijing China School of Microelectronics University of Chinese Academy of Sciences Beijing China Cognitive Computing Technology Joint Laboratory Wave Group Beijing China

ISBN: (纸本)9781728188089;9781728188096

Local feature descriptors exhibit great superiority in finger vein recognition due to their stability and robustness against local changes in images. However, most of these are methods use general-purpose descriptors that do not consider finger vein-specific features. In this work, we propose a finger vein-specific local feature descriptors based physiological characteristic of finger vein patterns, i.e., histogram of oriented physiological Gabor responses (HOPGR), for finger vein recognition. First, a prior of directional characteristic of finger vein patterns is obtained in an unsupervised manner. Then the physiological Gabor filter banks are set up based on the prior information to extract the physiological responses and orientation. Finally, to make the feature robust against local changes in images, a histogram is generated as output by dividing the image into non-overlapping cells and overlapping blocks. Extensive experimental results on several databases clearly demonstrate that the proposed method outperforms most current state-of-the-art finger vein recognition methods.

关键词： Histograms Image recognition Databases Veins Fingers Physiology Stability analysis

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Research on Convolution Decomposition and Hardware Acceleration based on FPGA

Research on Convolution Decomposition and Hardware Accelerat...

引用

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

引用

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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An FPGA-Based High-Throughput Dataflow Accelerator for Lightweight neural network

An FPGA-Based High-Throughput Dataflow Accelerator for Light...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Zhiyuan Zhao Jixing Li Gang Chen Zhelong Jiang Ruixiu Qiao Peng Xu Yihao Chen Huaxiang Lu School of Microelectronics University of Science and Technology of China Hefei China Institute of Semiconductors Chinese Academy of Sciences Beijing China Materials and Optoelectronics Research Center University of Chinese Academy of Sciences Beijing 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

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

Lightweight neural networks (LWNNs) have drawn significant attention recently for compact architecture and acceptable accuracy. Despite achieving substantial reductions in computation complexity and model size, increased memory access demands are caused by the extensive use of depthwise separable convolutions (DSCs) and skip-connection blocks (SCBs), which makes it difficult to achieve the anticipated performance. To process LWNNs efficiently, an FPGA-based dataflow accelerator is proposed in this paper. Firstly, a pixel-based streaming strategy is introduced to reduce off-chip memory access while minimizing on-chip memory overhead. Furthermore, an adaptive bandwidth computing engine (CE) is designed to increase computational efficiency in multi-CE architecture. Finally, based on the scalable CE, a dynamic parallelism allocation algorithm is proposed to avoid underutilization of on-chip computing resources. Shuf-fleNetV2 is implemented on Xilinx ZC706 platform, and the results show the proposed accelerator can achieve a state-of-the-art performance of 1771.2 FPS and computational efficiency of 0.64 GOPS/DSP, which is 5.3× of the reference design.

关键词： Performance evaluation Heuristic algorithms neural networks Bandwidth Computer architecture Parallel processing Computational efficiency

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Fitting objects with implicit polynomials by deep neural network

引用

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

引用

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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Optimizing FPGA-Based DCN Accelerator with On-Chip Dataflow Reordering and Serial-Parallel computing Array

Optimizing FPGA-Based DCN Accelerator with On-Chip Dataflow ...

引用

High Performance Big Data and intelligent Systems (HPBD&IS)

作者： Ming Zhang Jian Xu Jinzhong He Hong Qin AnnLab Institute of Semiconductors Chinese Academy of Sciences Beijing China Beijing Key Laboratory of Semiconductor Neural Network Intelligent Sensing and Computing Technology Beijing China College of Materials Science and Opto-Electronic Technology & School of Integrated Circuits University of Chinese Academy of Sciences Beijing China

Deep convolutional neural networks (DCNs) have recently experienced rapid development in the direction of lightweight and edge deployment. However, accelerators for DCNs face challenges in balancing computational and data bandwidth, leading to inefficient computation and high hardware costs. Additionally, different network structures make it challenging to design and reconfigure accelerators flexibly. To address these issues, this paper proposes a parallel-serial channel accelerator system, which resolves the low utilization of multipliers caused by small channels and inadequate bandwidth of fully connected layers. The results demonstrate that the proposed accelerator in this study maintains high computational performance and efficiency on typical DCNs. When implemented on Xilinx VCU128 at 200 MHz, the peak computational performance reaches 204.5 GOPS, with an efficiency of 0.37 GOPS/DSP and a maximum utilization rate of computing array up to 99.63%, surpassing previous works.

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Larger receptive Field and Context Information for Pose estimation:Larger Gaussian kernel 5

Larger receptive Field and Context Information for Pose esti...

引用

5th International Conference on High Performance Big Data and intelligent Systems, HDIS 2023

作者： Ma, Junxiao Yang, Kai Ke, Zunwang Wang, Gang Zhang, Yugui Cao, Fengcai Zhou, Qi Fei, Yang School of Information Beijing Forestry University Beijing100083 China CGNPC Uranium Industry Development Co. Ltd. Beijing100020 China School of Software Xinjiang University Urumqi830046 China School of Computing and Data Engineering NingboTech University Ningbo315100 China AnnLab Institute of Semiconductors Chinese Academy of Sciences Beijing100083 China Beijing Key Laboratory of Semiconductor Neural Network Intelligent Sensing and Computing Technology Beijing100083 China School of Integrated Circuits University of Chinese Academy of Sciences Beijing100049 China Co. Ltd Beijing100176 China School of Semiconductor Science and Technology South China Normal University GuangZhou510631 China

ISBN: (纸本)9798350394160

The field of pose estimation has a wide range of application prospects in various industries in the current era. With the continuous development of deep learning techniques, the effects in the field of human pose estimation are constantly being optimized. However, due to the influence of factors such as occlusion and human posture variability, it is easy to lead to inaccurate identification of keypoints and even keypoints’ missing and matching errors, there is still a lot of optimization space for the effect of pose estimation. In this paper, we propose an improvement to generate heatmaps in the training process: increase the radius of the Gaussian kernel and adjust the smoothness of Gaussian distribution to get a larger receptive field, encourage the model to use more context information in prediction, and control other parameters unchanged on the premise of using heatmap regression. Improve keypoint detection performance by optimizing this item. In the experiment, for the COCO-Keypoint data set, we selected different methods published in recent years, and after our improvement, we obtained better accuracy and performance than the original method, which proves the validity of our method. ©2023 IEEE.

关键词： Gaussian distribution

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