Cooperative sensing and heterogeneous information fusion are critical to realize vehicular cyber-physical systems (VCPSs). This paper makes the first attempt to quantitatively measure the quality of VCPS by designing ...
详细信息
Graph mining is becoming increasingly important due to the ever-increasing demands on analyzing complex structures in graphs. Existing graph accelerators typically hold most of the randomly-accessed data in an on-chip...
详细信息
ISBN:
(数字)9781728173832
ISBN:
(纸本)9781728173849
Graph mining is becoming increasingly important due to the ever-increasing demands on analyzing complex structures in graphs. Existing graph accelerators typically hold most of the randomly-accessed data in an on-chip memory to avoid off-chip communications. However, graph mining exhibits substantial random accesses from not only vertex dimension but also edge dimension (with the latter being excessively more complex than the former), leading to significant degradations in terms of both performance and energy *** observe that the most random memory requests arising in graph mining come from accessing a small fraction of valuable (vertex and edge) data when handling real-world graphs. To exploit this extension locality with maximum parallelism, we architect GRAMER, the first graph mining accelerator. GRAMER contains a specialized memory hierarchy, where the valuable data (precisely identified through a cost-efficient heuristic) is permanently resident in a high-priority memory while others are maintained in a cache-like memory under a lightweight replacement policy. The specific pipelined processing units are carefully designed to maximize computational parallelism. GRAMER is also equipped with a work-stealing mechanism to reduce load imbalance. We have implemented GRAMER on a Xilinx Alveo U250 accelerator card. Compared with two state-of-the-art CPU-based graph mining systems, Fractal and RStream, running on a 14-core Intel E5-2680 v4 processor, GRAMER achieves not only considerable speedups (1.11 × ~ 129.95 ) but also significant energy savings (5.79 × ~ 678.34×)
作者：
Bin YuanYan JiaLuyi XingDongfang ZhaoXiaoFeng WangDeqing ZouHai JinYuqing ZhangSchool of Cyber Science and Engineering
Huazhong Univ. of Sci. & Tech. China and National Engineering Research Center for Big Data Technology and System Cluster and Grid Computing Lab Services Computing Technology and System Lab and Big Data Security Engineering Research Center and Indiana University Bloomington and Shenzhen Huazhong University of Science and Technology Research Institute China School of Cyber Engineering
Xidian University China and National Computer Network Intrusion Protection Center University of Chinese Academy of Sciences China and Indiana University Bloomington Indiana University BloomingtonSchool of Cyber Science and Engineering
Huazhong Univ. of Sci. & Tech. China and National Engineering Research Center for Big Data Technology and System Cluster and Grid Computing Lab Services Computing Technology and System Lab and Big Data Security Engineering Research Center School of Computer Science and Technology
Huazhong Univ. of Sci. & Tech. China and National Engineering Research Center for Big Data Technology and System Cluster and Grid Computing Lab Services Computing Technology and System Lab and Big Data Security Engineering Research Center Huazhong Univ. of Sci. & Tech. China National Computer Network Intrusion Protection Center
University of Chinese Academy of Sciences China and School of Cyber Engineering Xidian University China
IoT clouds facilitate the communication between IoT devices and users, and authorize users' access to their devices. In this paradigm, an IoT device is usually managed under a particular IoT cloud designated by th...
ISBN:
(纸本)9781939133175
IoT clouds facilitate the communication between IoT devices and users, and authorize users' access to their devices. In this paradigm, an IoT device is usually managed under a particular IoT cloud designated by the device vendor, e.g., Philips bulbs are managed under Philips Hue cloud. Today's mainstream IoT clouds also support device access delegation across different vendors (e.g., Philips Hue, LIFX, etc.) and cloud providers (e.g., Google, IFTTT, etc.): for example, Philips Hue and SmartThings clouds support to delegate device access to another cloud such as Google Home, so a user can manage multiple devices from different vendors all through Google Home. Serving this purpose are the IoT delegation mechanisms developed and utilized by IoT clouds, which we found are heterogeneous and ad-hoc in the wild, in the absence of a standardized delegation protocol suited for IoT environments. In this paper, we report the first systematic study on real-world IoT access delegation, based upon a semi-automatic verification tool we developed. Our study brought to light the pervasiveness of security risks in these delegation mechanisms, allowing the adversary (e.g., Airbnb tenants, former employees) to gain unauthorized access to the victim's devices (e.g., smart locks) or impersonate the devices to trigger other devices. We confirmed the presence of critical security flaws in these mechanisms through end-to-end exploits on them, and further conducted a measurement study. Our research demonstrates the serious consequences of these exploits and the security implications of the practice today for building these mechanisms. We reported our findings to related parties, which acknowledged the problems. We further propose principles for developing more secure cross-cloud IoT delegation services, before a standardized solution can be widely deployed.
Nanomagnets are widely used to store information in non-volatile spintronic *** waves can transfer information with low-power consumption as their propagations are independent of charge ***,to dynamically couple two d...
详细信息
Nanomagnets are widely used to store information in non-volatile spintronic *** waves can transfer information with low-power consumption as their propagations are independent of charge ***,to dynamically couple two distant nanomagnets via spin waves remains a major challenge for *** we experimentally demonstrate coherent coupling of two distant Co nanowires by fast propagating spin waves in an yttrium iron garnet thin film with sub-50 nm *** in two nanomagnets are unidirectionally phase-locked with phase shifts controlled by magnon spin torque and spin-wave *** coupled system is finally formulated by an analytical theory in terms of an effective non-Hermitian *** results are attractive for analog neuromorphic computing that requires unidirectional information transmission.
The persistent memory (PM) requires maintaining the crash consistency and encrypting data, to ensure data recoverability and data confidentiality. The enforcement of these two goals does not only put more burden on pr...
详细信息
ISBN:
(数字)9783981926347
ISBN:
(纸本)9781728144689
The persistent memory (PM) requires maintaining the crash consistency and encrypting data, to ensure data recoverability and data confidentiality. The enforcement of these two goals does not only put more burden on programmers but also degrades performance. To address this issue, we propose a hardware-assisted encrypted persistent memory system. Specifically, logging and data encryption are assisted by hardware. Furthermore, we apply the counter-based encryption and the cipher feedback (CFB) mode encryption to data and log respectively, reducing the encryption overhead. Our primary experimental results show that the transaction throughput of the proposed design outperforms the baseline design by up to 34.4%.
Traditional machine learning algorithms cannot adequately train the parameters of networks using massive data. A deep convolutional neural network based on multi-parameter optimization by the TensorFlow deep learning ...
详细信息
Low-light image enhancement (LLIE) aims to improve low-illumination images. However, existing methods face two challenges: (1) uncertainty in restoration from diverse brightness degradations; (2) loss of texture and c...
详细信息
Low-light image enhancement (LLIE) aims to improve low-illumination images. However, existing methods face two challenges: (1) uncertainty in restoration from diverse brightness degradations; (2) loss of texture and color information caused by noise suppression and light enhancement. In this paper, we propose a novel enhancement approach, CodeEnhance, by leveraging discrete codebook priors and image refinement to address these challenges. In particular, we reframe LLIE as learning an image-to-code mapping from low-light images to discrete codebook, which has been learned from high-quality images. To enhance this process, a Semantic Embedding Module (SEM) is introduced to integrate semantic information with low-level features, and a Codebook Shift (CS) mechanism, designed to adapt the pre-learned codebook to better suit the distinct characteristics of our low-light dataset. Additionally, we present an Interactive Feature Transformation (IFT) module to refine texture and color information during image reconstruction, allowing for interactive enhancement based on user preferences. Extensive experiments on both real-world and synthetic benchmarks demonstrate that the incorporation of prior knowledge and controllable information transfer significantly enhances LLIE performance in terms of quality and fidelity. The proposed CodeEnhance exhibits superior robustness to various degradations, including uneven illumination, noise, and color distortion. The code can be obtained from https://***/csxuwu/CodeEnhance or https://***/*** .
As the latest generation of digital video coding standard, HEVC has technically optimized multiple modules such as related frame prediction, block processing and entropy coding in the frequency coding and decoding fra...
详细信息
As the latest generation of digital video coding standard, HEVC has technically optimized multiple modules such as related frame prediction, block processing and entropy coding in the frequency coding and decoding framework. However, flexible and efficient coding algorithms make the amount of calculation in the video decoding and reconstruction process increase dramatically. The energy efficiency of traditional processors is limited, and the decoding calculation process is difficult to meet the current needs of ultra-high-definition video playback. For the most important and time-consuming bitstream analysis and entropy decoding part of the HEVC decoding process, a new hardware architecture strategy is provided, which can effectively improve the HEVC decoding performance. In this paper, the designed data access module, bitstream analysis and entropy decoding unit are simulated and verified. Build an FPGA verification platform, and use the main tier standard test sequence to simulate and verify the designed hardware acceleration architecture. The experimental results show that the hardware architecture of bitstream analysis and entropy decoding designed in this paper can reach the functions and performance indicators specified by the HEVC standard Level-4 main tier, and the bitstream analysis acceleration effect is good. This thesis aims at the current HEVC decoding calculation characteristics, and through the design of hardware architecture verification, it provides new solutions and hardware architecture ideas for subsequent HEVC encoding and decoding performance optimization.
Android malware has become a serious threat for our daily life, and thus there is a pressing need to effectively mitigate or defend against them. Recently, many approaches and tools to analyze Android malware have bee...
详细信息
Accelerated multi-modal magnetic resonance (MR) imaging is a new and effective solution for fast MR imaging, providing superior performance in restoring the target modality from its undersampled counterpart with guida...
详细信息
暂无评论