A forensic autopsy is a surgical process in which experts collect a deceased body's internal and external information. These death certificates are the source of timely warnings of an increase in disease activity....
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Computing in memory (CIM) has been demonstrated promising for energy efficient computing. However, the dramatic growth of the data scale in neural network processors has aroused a demand for CIM architecture of higher...
Computing in memory (CIM) has been demonstrated promising for energy efficient computing. However, the dramatic growth of the data scale in neural network processors has aroused a demand for CIM architecture of higher bit density, for which the spin transfer torque magnetic RAM (STT-MRAM) with high bit density and performance arises as an up-and-coming candidate solution. In this work, we propose an analog CIM scheme based on tandem array within STT-MRAM (TAM) to further improve energy efficiency while achieving high bit density. First, the resistance summation based analog MAC operation minimizes the effect of low tunnel magnetoresistance (TMR) by the serial magnetic tunnel junctions (MTJs) structure in the proposed tandem array with smaller area overhead. Moreover, a read scheme of resistive-to-binary is designed to achieve the MAC results accurately and reliably. Besides, the data-dependent error caused by MTJs in series has been eliminated with a proposed dynamic selection circuit. Simulation results of a 2Kb TAM architecture show 113.2 TOPS/W and 63.7 TOPS/W for 4-bit and 8-bit input/weight precision, respectively, and reduction by 39.3% for bit-cell area compared with existing array of MTJs in series.
NVMe SSDs increase the performance of I/O system significantly, but they still suffer from the problem of performance jitters. Although existing studies adopt optimized GC or schedulers to make up for the vulnerabilit...
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NVMe SSDs increase the performance of I/O system significantly, but they still suffer from the problem of performance jitters. Although existing studies adopt optimized GC or schedulers to make up for the vulnerabilities, they cannot provide a near “GC-free” performance under various scenarios. In this paper, we propose SWin, a Set-Window division mechanism to alleviate I/O jitters of NVMe SSDs, in which we build an optimized scheduler with cache management on sets. First, we first divide NVMe SSD into set level and determine time windows based on sets. Then, an efficient I/O scheduler is designed to process requests of applications. Finally, we develop a host cache management strategy to ensure a stable response time for I/O requests. The experimental results indicate that the OCSSD system integrated with SWin can gain performance improvement by 32%, 34% and 14% in completion time, IOPS and latency performance, respectively.
Low-light image enhancement aims to restore the under-exposure image captured in dark scenarios. Under such scenarios, traditional frame-based cameras may fail to capture the structure and color information due to the...
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In this article, a robust fuzzy RBF neural network sliding-mode control with actor-critic for a class of robot systems. Trajectory tracking control of robotic systems has favorable performance for tracking control. Th...
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ISBN:
(纸本)9781665454209
In this article, a robust fuzzy RBF neural network sliding-mode control with actor-critic for a class of robot systems. Trajectory tracking control of robotic systems has favorable performance for tracking control. The fuzzy RBF neural network sliding-mode and actor-critic method is handled to compensate the uncertainty and disturbance of system. The stability analysis is based on for the proposed adaptive and robust control method. The simulation results show the effectiveness under the uncertainties.
Characteristics of long-running applications in cloud and big data environment are various and significantly influence the performance of cache *** gap between existing cache systems and the increasing performance req...
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Characteristics of long-running applications in cloud and big data environment are various and significantly influence the performance of cache *** gap between existing cache systems and the increasing performance requirements motivates us to propose the Application-oriented cache allocation and prefetching method(ACAP) to improve data access performance. An application-oriented cache allocation approach is designed based on hit count growth rates for a higher overall hit rate. Two application-oriented sequential prefetching approaches are proposed to improve the hit rate and prefetching accuracy by learning average read sizes of long-running applications. Based on correlation of data accesses, a parallelized correlated-directed prefetching approach is proposed to further increase the hit *** approaches are intergrated to obtain the maximized hit rate and prefetching accuracy. Experimental results on12 public real system traces show that ACAP achieves14.03%(up to 33.82%) higher prefetching accuracy and2.01%(up to 7.54%) higher hit rate compared with the best combination of baselines.
The saddle point matrices arising from many scientific computing fields have block structure W = (Equation presented), where the sub-block A is symmetric and positive definite, and C is symmetric and semi-nonnegative ...
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Image super-resolution (SR) is a technique to recover lost high-frequency information in low-resolution (LR) images. Spatial-domain information has been widely exploited to implement image SR, so a new trend is to inv...
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As a highly ill-posed issue, single image super-resolution (SISR) has been widely investigated in recent years. The main task of SISR is to recover the information loss caused by the degradation procedure. According t...
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Human face to anime face translation has attracted the attention of many researchers in recent years, and various works have achieved high-quality style transfer on conventional tasks. However, existing works often ha...
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ISBN:
(纸本)9781450385886
Human face to anime face translation has attracted the attention of many researchers in recent years, and various works have achieved high-quality style transfer on conventional tasks. However, existing works often have fatal shortcomings when the target domain training data is heavily insufficient, which is named as imbalanced setting. Here the imbalanced (low-resource) task, generally means there is no sufficient data on the training dataset compared with the conventional task, e.g. the training data size is less than 100. To solve this problem, we propose a multi-modal translation model for a specific style. Based on the cyclic adversarial network and class activation map, we import semantic modality to enhance data information and attention modules, which help the model focus more on the discriminative areas between source and target domain. The experimental results show that our method has superiority in low-resource settings compared with similar existing work.
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