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3D object Detection with LiDAR-camera encounters overfitting in algorithm development derived from violating some fundamental rules. We refer to the data annotation in dataset construction for theoretical optimization and argue that the regression ...

Today, convolutional neural networks (CNNs) are widely used in computer vision applications. However, the trends of higher accuracy and higher resolution generate larger networks. The requirements of computation or I/O are the key bottlenecks. In this ...

In Database-as-a-Service (DBaaS) clusters, resource management is a complex optimization problem that assigns tenants to nodes, subject to various constraints and objectives. Tenants share resources within a node, however, their resource demands can ...

Oversubscription is an essential cost management strategy for cloud database providers, and its importance is magnified by the emerging paradigm of serverless databases. In contrast to general purpose techniques used for oversubscription in hypervisors, ...

Relational cloud Database-as-a-Service offerings run on multi-tenant infrastructure consisting of clusters of nodes, with each node hosting multiple tenant databases. Such clusters may be over-subscribed to increase resource utilization and improve ...

Multi-task learning (MTL) aims to learn shared representations from multiple tasks simultaneously, which has yielded outstanding performance in widespread applications of computer vision. However, existing multi-task approaches often demand manual ...

Scale variation stands out as one of key challenges in face detection. Recent attempts have been made to cope with this issue by incorporating image/feature pyramids or adjusting anchor sampling/matching strategies. In this work, we propose a ...

Low bit quantization of neural network is required on edge devices to achieve lower power consumption and higher performance. 8bit or binary network either consumes a lot of resources or has accuracy degradation. Thus, a full-process hardware-friendly ...

Neural Architecture Search (NAS) is a promising approach to discover good neural network architectures for given applications. Among the three basic components in a NAS system (search space, search strategy, and evaluation), prior work mainly focused on ...

In recent years, Convolutional Neural Network(CNN) is becoming the state-of-the-art method in a wide range of Artificial Intelligence(AI) domains. The increasingly large and complex CNN models are both computation bound and I/O bound. FPGA-based ...

Neural Networks (NNs) have made a significant breakthrough in many fields, while they also pose a great challenge to hardware platforms since the state-of-the-art neural networks are both communicational- and computational-intensive. Researchers ...

Compared with H.264, High Efficient Video Coding (HEVC) improves the coding efficiency by 50% at the price of significant increase in encoding time, due to Rate Distortion Optimization (RDO) on large variations of block sizes and prediction modes. In this ...

Approximate computing is a promising design paradigm for better performance and power efficiency. In this paper, we propose a power efficient framework for analog approximate computing with the emerging metal-oxide resistive switching random-access memory ...

MapReduce is widely acknowledged by both industry and academia as an effective programming model for query processing on big data. It is crucial to design an optimizer which finds the most efficient way to execute an SQL query using MapReduce. However, ...

Cloud computing is becoming a major trend for delivering and accessing infrastructure on demand via the network. Meanwhile, the usage of FPGAs (Field Programmable Gate Arrays) for computation acceleration has made significant inroads into multiple ...

Domain of stereo vision is highly important in the fields of autonomous cars, video tolling, robotics, and aerial surveys. The specific feature of this domain is that we should handle not only the pixel-by-pixel 2D processing in one image but also the ...

The cessation of Moore's Law has limited further improvements in power efficiency. In recent years, the physical realization of the memristor has demonstrated a promising solution to ultra-integrated hardware realization of neural networks, which can be ...

Sparse matrix factorization is a critical step for the circuit simulation problem, since it is time consuming and computed repeatedly in the flow of circuit simulation. To accelerate the factorization of sparse matrices, a parallel CPU+FPGA based ...

The research on complex Brain Networks plays a vital role in understanding the connectivity patterns of the human brain and disease-related alterations. Recent studies have suggested a noninvasive way to model and analyze human brain networks by using ...

It is generally believed that Zipf’s law is well applied for Web objects. However, as OSNs prevail and Internet begins to concentrate more and more on individuals, many researchers report that request frequency of Web objects deviates from Zipf’s law ...