Alibaba Group

About: Alibaba Group is a company organization based out in Hangzhou, China. It is known for research contribution in the topics: Computer science & Terminal (electronics). The organization has 6810 authors who have published 7389 publications receiving 55653 citations. The organization is also known as: Alibaba Group Holding Limited & Alibaba Group (Cayman Islands).

UniFuse: Unidirectional Fusion for 360° Panorama Depth Estimation

Abstract: Learning depth from spherical panoramas is becoming a popular research topic because a panorama has a full field-of-view of the environment and provides a relatively complete description of a scene. However, applying well-studied CNNs for perspective images to the standard representation of spherical panoramas, i.e. , the equirectangular projection, is suboptimal, as it becomes distorted towards the poles. Another representation is the cubemap projection, which is distortion-free but discontinued on edges and limited in the field-of-view. This letter introduces a new framework to fuse features from the two projections, unidirectionally feeding the cubemap features to the equirectangular features only at the decoding stage. Unlike the recent bidirectional fusion approach operating at both the encoding and decoding stages, our fusion scheme is much more efficient. Besides, we also designed a more effective fusion module for our fusion scheme. Experiments verify the effectiveness of our proposed fusion strategy and module, and our model achieves state-of-the-art performance on four popular datasets. Additional experiments show that our model also has the advantages of model complexity and generalization capability.

Gradient Centralization: A New Optimization Technique for Deep Neural Networks

Abstract: Optimization techniques are of great importance to effectively and efficiently train a deep neural network (DNN). It has been shown that using the first and second order statistics (e.g., mean and variance) to perform Z-score standardization on network activations or weight vectors, such as batch normalization (BN) and weight standardization (WS), can improve the training performance. Different from these existing methods that mostly operate on activations or weights, we present a new optimization technique, namely gradient centralization (GC), which operates directly on gradients by centralizing the gradient vectors to have zero mean. GC can be viewed as a projected gradient descent method with a constrained loss function. We show that GC can regularize both the weight space and output feature space so that it can boost the generalization performance of DNNs. Moreover, GC improves the Lipschitzness of the loss function and its gradient so that the training process becomes more efficient and stable. GC is very simple to implement and it can be embedded into existing gradient based DNN optimizers with only one line of code. Our experiments on various applications, including general image classification, fine-grained image classification, detection and segmentation, demonstrate that GC can consistently improve the performance of DNN learning. The code of GC can be found at https://github.com/Yonghongwei/Gradient-Centralization.

Guaranteeing Deadlines for Inter-Data Center Transfers

Abstract: Inter-data center wide area networks (inter-DC WANs) carry a significant amount of data transfers that require to be completed within certain time periods, or deadlines. However, very little work has been done to guarantee such deadlines. The crux is that the current inter-DC WAN lacks an interface for users to specify their transfer deadlines and a mechanism for provider to ensure the completion while maintaining high WAN utilization. In this paper, we address the problem by introducing a deadline-based network abstraction (DNA) for inter-DC WANs. DNA allows users to explicitly specify the amount of data to be delivered and the deadline by which it has to be completed. The malleability of DNA provides flexibility in resource allocation. Based on this, we develop a system called Amoeba that implements DNA. Our simulations and test bed experiments show that Amoeba , by harnessing DNA’s malleability, accommodates 15% more user requests with deadlines, while achieving 60% higher WAN utilization than prior solutions.

A Short-Term Rainfall Prediction Model Using Multi-task Convolutional Neural Networks

Abstract: Precipitation prediction, such as short-term rainfall prediction, is a very important problem in the field of meteorological service. In practice, most of recent studies focus on leveraging radar data or satellite images to make predictions. However, there is another scenario where a set of weather features are collected by various sensors at multiple observation sites. The observations of a site are sometimes incomplete but provide important clues for weather prediction at nearby sites, which are not fully exploited in existing work yet. To solve this problem, we propose a multi-task convolutional neural network model to automatically extract features from the time series measured at observation sites and leverage the correlation between the multiple sites for weather prediction via multi-tasking. To the best of our knowledge, this is the first attempt to use multi-task learning and deep learning techniques to predict short-term rainfall amount based on multi-site features. Specifically, we formulate the learning task as an end-to-end multi-site neural network model which allows to leverage the learned knowledge from one site to other correlated sites, and model the correlations between different sites. Extensive experiments show that the learned site correlations are insightful and the proposed model significantly outperforms a broad set of baseline models including the European Centre for Medium-range Weather Forecasts system (ECMWF).

An Efficient Hardware Accelerator for Sparse Convolutional Neural Networks on FPGAs

Abstract: Deep convolutional neural networks (CNN) have achieved remarkable performance with the cost of huge computation. As the CNN model becomes more complex and deeper, compressing CNN to sparse by pruning the redundant connection in networks has emerged as an attractive approach to reduce the amount of computation and memory requirement. In recent years, FPGAs have been demonstrated to be an effective hardware platform to accelerate CNN inference. However, most existing FPGA architectures focus on dense CNN models. The architecture designed for dense CNN models are inefficient when executing sparse models as most of the arithmetic operations involve addition and multiplication with zero operands. On the other hand, recent sparse FPGA accelerators only focus on FC layers. In this work, we aim to develop an FPGA accelerator for sparse CNNs. To efficiently deal with the irregular connection in the sparse convolutional layer, we propose a weight-oriented dataflow that processes each weight individually. Then we design an FPGA architecture which can handle input-weight connection and weight-output connection efficiently. For input-weight connection, we design a tile look-up table to eliminate the runtime indexing match of compressed weights. Moreover, we develop a weight layout to enable high on-chip memory access. To cooperate with the weight layout, a channel multiplexer is inserted to locate the address which can ensure no data access conflict. Experiments demonstrate that our accelerator can achieve 223.4-309.0 GOP/s for the modern CNNs on Xilinx ZCU102, which provides a 3.6x-12.9x speedup over previous dense CNN FPGA accelerators.