In this paper, we propose an in-memory computing framework (called GPF) that provides a set of genomic formats, APIs and a fast genomic engine for large-scale genomic data processing. Our GPF comprises two main compon...
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Replicas 1 of a vertex play an important role in existing distributed graph processing systems which make a single vertex to be parallel processed by multiple machines and access remote neighbors locally without any r...
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The complexity and diversity of big data and AI workloads make understanding them difficult and challenging. This paper proposes a new approach to modelling and characterizing big data and AI workloads. We consider ea...
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As cloud computing is moving forward rapidly, cloud providers have been encountering great challenges： long tail latency, low utilization, and high interference. They intend to co-locate multiple workloads on a singl...
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As cloud computing is moving forward rapidly, cloud providers have been encountering great challenges： long tail latency, low utilization, and high interference. They intend to co-locate multiple workloads on a single server to improve the resource utilization. But the co-located applications suffer from severe performance interference and long tail latency, which lead to unpredictable user experience. To meet these challenges, software-defined cloud has been proposed to facilitate tighter coordination among application, operating system and hardware. Users＇ quality of service （QoS） requirements could be propagated all the way down to the hardware with differential management mechanisms. However, there is little hardware support to maintain and guarantee users＇ QoS requirements. To this end, this paper proposes Labeled von Neumann architecture （LvNA）, which introduces a labelling mechanism to convey more software＇s semantic information such as QoS and security to the underlying hardware. LvNA is able to correlate labels with various entities, e.g., virtual machine, process and thread, and propagate labels in the whole machine and program differentiated services based on rules. We consider LvNA to be a fundamental hardware support to the software-defined cloud.
A new based on Semi-supervised classification theory for SAR images in contourlet domain is proposed, in this paper. Attempting to get better and faster performance, the PSO algorithm (Particle swarm optimization algo...
A new based on Semi-supervised classification theory for SAR images in contourlet domain is proposed, in this paper. Attempting to get better and faster performance, the PSO algorithm (Particle swarm optimization algorithm) and contourlet domain is proposed to instead of traditional k-means algorithm. PSO is used to find the global optimum by performing a global search in the whole solution space. And then, contourlet is applied in front of construct the similarity matrix to extract more effective eigenvalues. In section five, the proposed algorithm got better classification results than the traditional k-means algorithm which is proved by experimental results show that in terms of running time, classification accuracy and Kappa coefficient.
Inference efficiency is the predominant consideration in designing deep learning accelerators. Previous work mainly focuses on skipping zero values to deal with remarkable ineffectual computation, while zero bits in n...
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Node2Vec is a state-of-the-art general-purpose feature learning method for network analysis. However, current solutions cannot run Node2Vec on large-scale graphs with billions of vertices and edges, which are common i...
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Semantic scene completion predicts volumetric occupancy and object category of a 3D scene, which helps intelligent agents to understand and interact with the surroundings. In this work, we propose a disentangled frame...
Semantic scene completion predicts volumetric occupancy and object category of a 3D scene, which helps intelligent agents to understand and interact with the surroundings. In this work, we propose a disentangled framework, sequentially carrying out 2D semantic segmentation, 2D-3D reprojection and 3D semantic scene completion. This three-stage framework has three advantages: (1) explicit semantic segmentation significantly boosts performance; (2) flexible fusion ways of sensor data bring good extensibility; (3) progress in any subtask will promote the holistic performance. Experimental results show that regardless of inputing a single depth or RGB-D, our framework can generate high-quality semantic scene completion, and outperforms state-of-the-art approaches on both synthetic and real datasets.
Motion blur is one of the most common degradation artifacts in dynamic scene photography. This paper reviews the NTIRE 2020 Challenge on Image and Video Deblurring. In this challenge, we present the evaluation results...
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The increasing attention on deep learning has tremendously spurred the design of intelligence processing hardware. The variety of emerging intelligence processors requires standard benchmarks for fair comparison and s...
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The increasing attention on deep learning has tremendously spurred the design of intelligence processing hardware. The variety of emerging intelligence processors requires standard benchmarks for fair comparison and system optimization （in both software and hardware）. However, existing benchmarks are unsuitable for benchmarking intelligence processors due to their non-diversity and nonrepresentativeness. Also, the lack of a standard benchmarking methodology further exacerbates this problem. In this paper, we propose BENCHIP, a benchmark suite and benchmarking methodology for intelligence processors. The benchmark suite in BENCHIP consists of two sets of benchmarks： microbenchmarks and macrobenchmarks. The microbenchmarks consist of single-layer networks, They are mainly designed for bottleneck analysis and system optimization. The macrobenchmarks contain state-of-the-art industrial networks, so as to offer a realistic comparison of different platforms. We also propose a standard benchmarking methodology built upon an industrial software stack and evaluation metrics that comprehensively reflect various characteristics of the evaluated intelligence processors, BENCHIP is utilized for evaluating various hardware platforms, including CPUs, GPUs, and accelerators. BENCHIP will be open-sourced soon.
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