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学者姓名:孙宏滨

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Architectural Exploration to Address the Reliability Challenges for ReRAM-Based Buffer in SSD EI Scopus SCIE
期刊论文 | 2019 , 66 (1) , 226-238 | IEEE Transactions on Circuits and Systems I: Regular Papers
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Abstract :

Hybrid solid state drive based on ReRAM and NAND flash technologies has shown promising performance and energy efficiency. In this application, ReRAM is mainly used as a non-volatile buffer to hold recently accessed data pages or address mapping information. The previous studies on the ReRAM-based buffer mainly focus on performance and efficiency improvement, while reliability issues are not taken into consideration. Nevertheless, according to our quantitative evaluation, the limited endurance and random bit errors pose challenges to ReRAM-based buffer design. For example, without wear leveling, the raw lifetime of ReRAM-based SSD buffer may be as low as 0.02 year for some specific I/O traces. Therefore, we propose two efficient architecture techniques, i.e., multi-bloom-filter-based wear leveling and hybrid error protection, to improve the lifetime and reduce the error protection cost of ReRAM-based buffer, respectively. Simulation results demonstrate that the proposed wear leveling technique can extend the lifetime of ReRAM-based buffer by $34.7x$ on average. The proposed hybrid error protection scheme can improve the response time by at least 4.2&#x0025; compared with other error protection schemes. IEEE

Keyword :

Address mappings Efficiency improvement Efficient architecture Non-volatile memory Prototypes Quantitative evaluation Random bit errors Solid state drives

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GB/T 7714 Zhao, Xiaoqing , Sun, Hongbin , Liu, Longjun et al. Architectural Exploration to Address the Reliability Challenges for ReRAM-Based Buffer in SSD [J]. | IEEE Transactions on Circuits and Systems I: Regular Papers , 2019 , 66 (1) : 226-238 .
MLA Zhao, Xiaoqing et al. "Architectural Exploration to Address the Reliability Challenges for ReRAM-Based Buffer in SSD" . | IEEE Transactions on Circuits and Systems I: Regular Papers 66 . 1 (2019) : 226-238 .
APA Zhao, Xiaoqing , Sun, Hongbin , Liu, Longjun , Yang, Yang , Dai, Liangliang , Wu, Xiulong et al. Architectural Exploration to Address the Reliability Challenges for ReRAM-Based Buffer in SSD . | IEEE Transactions on Circuits and Systems I: Regular Papers , 2019 , 66 (1) , 226-238 .
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Spatial-temporal neural networks for action recognition EI Scopus
会议论文 | 2018 , 519 , 619-627 | 14th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2018
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Abstract :

Action recognition is an important yet challenging problem in many applications. Recently, neural network and deep learning approaches have been widely applied to action recognition and yielded impressive results. In this paper, we present a spatial-temporal neural network model to recognize human actions in videos. This network is composed of two connected structures. A two-stream-based network extracts appearance and optical flow features from video frames. This network characterizes spatial information of human actions in videos. A group of LSTM structures following the spatial network describe the temporal information of human actions. We test our model with data from two public datasets and the experimental results show that our method improves the action recognition accuracy compared to the baseline methods. © IFIP International Federation for Information Processing 2018 Published by Springer International Publishing AG 2018. All Rights Reserved.

Keyword :

Action recognition Connected structures Learning approach LSTM Spatial informations Spatial temporals Spatial-temporal structure Temporal information

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GB/T 7714 Jing, Chao , Wei, Ping , Sun, Hongbin et al. Spatial-temporal neural networks for action recognition [C] . 2018 : 619-627 .
MLA Jing, Chao et al. "Spatial-temporal neural networks for action recognition" . (2018) : 619-627 .
APA Jing, Chao , Wei, Ping , Sun, Hongbin , Zheng, Nanning . Spatial-temporal neural networks for action recognition . (2018) : 619-627 .
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Exploring the Potential of Using Semantic Context and Common Sense in On-Road Vehicle Detection EI
会议论文 | 2018 , 2018-June , 2111-2116 | 2018 IEEE Intelligent Vehicles Symposium, IV 2018
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Abstract :

Vehicle detection is an important research topic for autonomous driving community. Since the great success of deep learning on object detection, almost all vehicle detection methods go along with this line. However, deep learning methods heavily rely on the training data, and the whole mechanism is like a 'black box' Therefore, in this paper, we explore a vehicle detection method using traffic semantic context and human common sense instead of relying on the training data. To verify our idea, we compare our method with two classic machine learning methods as well as three state- of-the-art deep learning methods on a dataset collected in real traffics. The results show that our method outperforms others on this dataset. The deep learning methods may exceed ours after enlarging the training data or testing on more complicated datasets. However, the main contribution of this paper is providing inspiration for learning methods, and we believe their performance can be greatly improved after considering the idea of this paper. © 2018 IEEE.

Keyword :

Autonomous driving Learning methods Machine learning methods Research topics Road vehicles Semantic context Training data Vehicle detection

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GB/T 7714 Nan, Zhixiong , Pan, Menghan , Wang, Xiao et al. Exploring the Potential of Using Semantic Context and Common Sense in On-Road Vehicle Detection [C] . 2018 : 2111-2116 .
MLA Nan, Zhixiong et al. "Exploring the Potential of Using Semantic Context and Common Sense in On-Road Vehicle Detection" . (2018) : 2111-2116 .
APA Nan, Zhixiong , Pan, Menghan , Wang, Xiao , Wei, Ping , Xu, Linhai , Sun, Hongbin et al. Exploring the Potential of Using Semantic Context and Common Sense in On-Road Vehicle Detection . (2018) : 2111-2116 .
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Leveraging Spatio-Temporal Evidence and Independent Vision Channel to Improve Multi-Sensor Fusion for Vehicle Environmental Perception EI
会议论文 | 2018 , 2018-June , 591-596 | 2018 IEEE Intelligent Vehicles Symposium, IV 2018
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Abstract :

For intelligent vehicles, multi-sensor fusion is of great importance to perceive traffic environment with high accuracy and robustness. In this paper, we propose two effective methods, i.e. spatio-temporal evidence generating and independent vision channel, to improve multi-sensor track-level fusion for vehicle environmental perception. The spatio-temporal evidence includes instantaneous evidence, tracking evidence and tracks matching evidence to improve existence fusion. Independent vision channel leverages the specific advantage of vision processing on object recognition to improve classification fusion. The proposed methods are evaluated by using the multi-sensor dataset collected from real traffic environment. Experimental results demonstrate that the proposed methods can significantly improve the multi-sensor track-level fusion in terms of both detection accuracy and classification accuracy. © 2018 IEEE.

Keyword :

Classification accuracy Classification fusion Detection accuracy Environmental perceptions Multi-sensor fusion Track level fusion Traffic environment Vision processing

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GB/T 7714 Shi, Juwang , Wang, Wenxiu , Wang, Xiao et al. Leveraging Spatio-Temporal Evidence and Independent Vision Channel to Improve Multi-Sensor Fusion for Vehicle Environmental Perception [C] . 2018 : 591-596 .
MLA Shi, Juwang et al. "Leveraging Spatio-Temporal Evidence and Independent Vision Channel to Improve Multi-Sensor Fusion for Vehicle Environmental Perception" . (2018) : 591-596 .
APA Shi, Juwang , Wang, Wenxiu , Wang, Xiao , Sun, Hongbin , Lan, Xuguang , Xin, Jingmin et al. Leveraging Spatio-Temporal Evidence and Independent Vision Channel to Improve Multi-Sensor Fusion for Vehicle Environmental Perception . (2018) : 591-596 .
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Efficient Rectangle Fitting of Sparse Laser Data for Robust On-Road Obiect Detection EI
会议论文 | 2018 , 2018-June , 846-853 | 2018 IEEE Intelligent Vehicles Symposium, IV 2018
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Abstract :

On-road object detection is one of the most important tasks for the autonomous driving of intelligent vehicle. Nevertheless, the previous methods based on 2D LIDAR sensor only focus on the detection of vehicles, and show severe limitations on the detection of other objects. Accordingly, this paper proposes an on-road object detection method, which employs rectangle fitting and concavity determination to improve the robustness of ob- ject detection. The proposed approaches are extensively evaluated by using the sparse laser data collected by 2D LIDAR from real traffic environment. Experimental results demonstrate that the proposed rectangle fitting outperforms the previous approaches in terms of both detection accuracy and computational efficiency. © 2018 IEEE.

Keyword :

Autonomous driving Detection accuracy Laser data LIDAR sensors Object detection method Real traffic

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GB/T 7714 Yang, Shuai , Xiang, Zhaohong , Wu, Jin et al. Efficient Rectangle Fitting of Sparse Laser Data for Robust On-Road Obiect Detection [C] . 2018 : 846-853 .
MLA Yang, Shuai et al. "Efficient Rectangle Fitting of Sparse Laser Data for Robust On-Road Obiect Detection" . (2018) : 846-853 .
APA Yang, Shuai , Xiang, Zhaohong , Wu, Jin , Wang, Xiao , Sun, Hongbin , Xin, Jinming et al. Efficient Rectangle Fitting of Sparse Laser Data for Robust On-Road Obiect Detection . (2018) : 846-853 .
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Exploring Resource-Aware Deep Neural Network Accelerator and Architecture Design CPCI-S
会议论文 | 2018 | 23rd IEEE International Conference on Digital Signal Processing (DSP)
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Abstract :

Due to the ever-increasing number of neural networks(NNs) connections and parameters, computation on neural networks is becoming both power hankering and memory intensive. In this paper, we propose a sparse neural networks accelerator to improve memory resource utilization and improve power efficiency. In contrast to prior works, we introduce a highly integrated software and hardware co-design technique that combines resource-aware software compression algorithms and specialized hardware inference engine in the accelerator. Compared with other designs, our design can compress parameters by 90x and substantially improve storage resource utilization, performance (6.9x) and power (1.2x) for NN accelerators.

Keyword :

Hardware acceleration Model. compression Convolution neural network FPGA

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GB/T 7714 Li, Baoting , Liu, Longjun , Liang, Jiahua et al. Exploring Resource-Aware Deep Neural Network Accelerator and Architecture Design [C] . 2018 .
MLA Li, Baoting et al. "Exploring Resource-Aware Deep Neural Network Accelerator and Architecture Design" . (2018) .
APA Li, Baoting , Liu, Longjun , Liang, Jiahua , Sun, Hongbin , Geng, Li , Zheng, Nanning . Exploring Resource-Aware Deep Neural Network Accelerator and Architecture Design . (2018) .
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Human-Like Maneuver Decision Using LSTM-CRF Model for On-Road Self-Driving CPCI-S
会议论文 | 2018 , 210-216 | 21st IEEE International Conference on Intelligent Transportation Systems (ITSC)
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Abstract :

In the near future, self-driving vehicles will be frequently tested in urban traffic, and will definitely coexist with human-driving vehicles. To harmoniously share traffic resources, self-driving vehicles need to respect behavioral customs of human drivers. Taking on-road driving for example, self-driving vehicles are supposed to behave in a human-like way to decide when to keep the lane and when to change the lane. This point, however, has not been well addressed in current on-road maneuver decision methods. In this paper, a human-like maneuver decision method based on Long Short Term Memory (LST-M) neural network and Conditional Random Field (CRF) model is proposed for on-road self-driving. Different from previous works, this paper considers the maneuver decision problem as a sequence labeling problem. Its input is a time-series vector which describes a period of neighboring traffic history, and its output is a one-hot vector indicates the suitable maneuver. The proposed model is trained on the NGSIM public dataset, which contains millions of driving maneuvers collected from thousands of human drivers. Simulations with manipulated conditions reveal human-like reasoning for maneuver decision inside the proposed model. Comparative experiments further demonstrate a better human-like performance achieved by the proposed method than that of previous methods.

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GB/T 7714 Wang, Xiao , Wu, Jinqiang , Gu, Yanlei et al. Human-Like Maneuver Decision Using LSTM-CRF Model for On-Road Self-Driving [C] . 2018 : 210-216 .
MLA Wang, Xiao et al. "Human-Like Maneuver Decision Using LSTM-CRF Model for On-Road Self-Driving" . (2018) : 210-216 .
APA Wang, Xiao , Wu, Jinqiang , Gu, Yanlei , Sun, Hongbin , Xu, Linhai , Kamijo, Shunsuke et al. Human-Like Maneuver Decision Using LSTM-CRF Model for On-Road Self-Driving . (2018) : 210-216 .
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VLSI Architecture Exploration of Guided Image Filtering for 1080P@60Hz Video Processing SCIE
期刊论文 | 2018 , 28 (1) , 230-241 | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
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Abstract :

Guided image filtering (GIF) is a promising edge-preserving filtering technique that has been applied in a variety of applications. Nevertheless, an efficient very-large-scale integration (VLSI) architecture design of GIF is still very challenging for the real-time processing of full-high definition videos. Previously proposed architectures are somewhat inefficient in terms of either on-chip memory usage or off-chip memory bandwidth. This paper aims to improve the balance between on-chip memory usage and off-chip memory bandwidth through architecture exploration. Three critical architectural tradeoffs in the VLSI design of GIF are explored, and two efficient VLSI architectures, namely sequential line-based and parallel line-based architectures, are proposed. Experimental results demonstrate that the proposed VLSI design only consumes 34.1-K logic gates, 25.4-KB on-chip memories, and 373-MB/s off-chip memory bandwidth while achieving a real-time video processing of 1080P@60Hz at the maximum clock frequency of 297-MHz. Moreover, the proposed VLSI circuits are fully pipelined and synchronized to the pixel clock of output video, so can be seamlessly integrated into diverse real-time video processing systems.

Keyword :

very-large-scale integration (VLSI) architecture memory hierarchy video processing Guided image filtering (GIF)

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GB/T 7714 Zhang, Xuchong , Sun, Hongbin , Chen, Shiqiang et al. VLSI Architecture Exploration of Guided Image Filtering for 1080P@60Hz Video Processing [J]. | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY , 2018 , 28 (1) : 230-241 .
MLA Zhang, Xuchong et al. "VLSI Architecture Exploration of Guided Image Filtering for 1080P@60Hz Video Processing" . | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 28 . 1 (2018) : 230-241 .
APA Zhang, Xuchong , Sun, Hongbin , Chen, Shiqiang , Zheng, Nanning . VLSI Architecture Exploration of Guided Image Filtering for 1080P@60Hz Video Processing . | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY , 2018 , 28 (1) , 230-241 .
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Worst Case Driven Display Frame Compression for Energy-Efficient Ultra-HD Display Processing EI SCIE Scopus
期刊论文 | 2018 , 20 (5) , 1113-1125 | IEEE TRANSACTIONS ON MULTIMEDIA
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Abstract :

Display frame compression is an effective technique to address the challenge of external memory access in ultrahigh definition video display system. Nevertheless, previously proposed display frame compression designs are inadequate in terms of either energy efficiency or throughput. This paper aims to exploit the algorithm and very large scale integration (VLSI) architecture of a worst case driven display frame compression. By using a prediction-and-compression framework and a semi-fixed length coding scheme, the proposed design can achieve the much better balance between compression efficiency and throughput, and substantially reduce the bandwidth requirement and energy consumption of external memory system in the meanwhile. Extensive experiments demonstrate that the proposed display frame compression achieves 5.7-dB peak signal-to-noise ratio improvement, 3.1% compression ratio reduction, 3 x throughput, and 66.4% hardware cost saving, compared with the best previous work. In addition, the proposed VLSI design can support the throughput of 4 K x 2 K@60 Hz and reduce at least 17.6% energy consumption of external memory system by exploiting dynamic voltage and frequency scaling, compared with conventional display frame compression works.

Keyword :

memory bandwidth Display frame compression energy efficiency ultra-high definition (HD) video very large scale integration (VLSI) architecture

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GB/T 7714 Chen, Qiubo , Sun, Hongbin , Zheng, Nanning . Worst Case Driven Display Frame Compression for Energy-Efficient Ultra-HD Display Processing [J]. | IEEE TRANSACTIONS ON MULTIMEDIA , 2018 , 20 (5) : 1113-1125 .
MLA Chen, Qiubo et al. "Worst Case Driven Display Frame Compression for Energy-Efficient Ultra-HD Display Processing" . | IEEE TRANSACTIONS ON MULTIMEDIA 20 . 5 (2018) : 1113-1125 .
APA Chen, Qiubo , Sun, Hongbin , Zheng, Nanning . Worst Case Driven Display Frame Compression for Energy-Efficient Ultra-HD Display Processing . | IEEE TRANSACTIONS ON MULTIMEDIA , 2018 , 20 (5) , 1113-1125 .
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A 4Kx2K@60fps Multi-format Multi-function Display Processor for High Perceptual Quality CPCI-S
会议论文 | 2018 , 427-430 | 14th IEEE Asia Pacific Conference on Circuits and Systems (APCCAS)
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Abstract :

This paper presents a video display processor which supports a variety of video formats and integrates multiple advanced functions for image quality improvement, including edge-directed image upscaling, guided image filter (GIF) based detail enhancement and noise reduction, multi-view autostereoscopic 3D processing, etc. By leveraging algorithm and architecture co-design, this work efficiently implements these computational intensive display processing functions in a single chip. The chip is fabricated in GF 55nm CMOS technology, and the core size is 38.71mm2 including 1.8M logic gates and 541KB SRAM. The chip works at the maximum operating frequency of 594MHz with the core supply voltage of 1.2V. The maximum input and output video formats reach up to 4Kx2K@60fps.

Keyword :

video display processing autostereoscopic 3D Ultra-high resolution detail enhancement image upscaling

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GB/T 7714 Wang, Hang , Sun, Hongbin , Zhang, Xuchong et al. A 4Kx2K@60fps Multi-format Multi-function Display Processor for High Perceptual Quality [C] . 2018 : 427-430 .
MLA Wang, Hang et al. "A 4Kx2K@60fps Multi-format Multi-function Display Processor for High Perceptual Quality" . (2018) : 427-430 .
APA Wang, Hang , Sun, Hongbin , Zhang, Xuchong , Chen, Qiubo , Ren, Pengju , Wu, Xiaogang et al. A 4Kx2K@60fps Multi-format Multi-function Display Processor for High Perceptual Quality . (2018) : 427-430 .
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