Kaiyong Zhao

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  • AutoML: A Survey of the State-of-the-Art

    Deep learning has penetrated all aspects of our lives and brought us great convenience. However, the process of building a high-quality deep learning system for a specific task is not only time-consuming but also requires lots of resources and relies on human expertise, which hinders the development of deep learning in both industry and academia. To alleviate this problem, a growing number of research projects focus on automated machine learning (AutoML). In this paper, we provide a comprehensive and up-to-date study on the state-of-the-art AutoML. First, we introduce the AutoML techniques in details according to the machine learning pipeline. Then we summarize existing Neural Architecture Search (NAS) research, which is one of the most popular topics in AutoML. We also compare the models generated by NAS algorithms with those human-designed models. Finally, we present several open problems for future research.

    08/02/2019 ∙ by Xin He, et al. ∙ 257 share

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  • A Distributed Synchronous SGD Algorithm with Global Top-k Sparsification for Low Bandwidth Networks

    Distributed synchronous stochastic gradient descent (S-SGD) with data parallelism requires very high communication bandwidth between computational workers (e.g., GPUs) to exchange gradients iteratively. Recently, Top-k sparsification techniques have been proposed to reduce the volume of data to be exchanged among workers and thus alleviate the network pressure. Top-k sparsification can zero-out a significant portion of gradients without impacting the model convergence. However, the sparse gradients should be transferred with their indices, and the irregular indices make the sparse gradients aggregation difficult. Current methods that use AllGather to accumulate the sparse gradients have a communication complexity of O(kP), where P is the number of workers, which is inefficient on low bandwidth networks with a large number of workers. We observe that not all Top-k gradients from P workers are needed for the model update, and therefore we propose a novel global Top-k (gTop-k) sparsification mechanism to address the difficulty of aggregating sparse gradients. Specifically, we choose global Top-k largest absolute values of gradients from P workers, instead of accumulating all local Top-k gradients to update the model in each iteration. The gradient aggregation method based on gTop-k sparsification, namely gTopKAllReduce, reduces the communication complexity from O(kP) to O(klog_2P). Through extensive experiments on different DNNs, we verify that gTop-k S-SGD has nearly consistent convergence performance with S-SGD. We evaluate the training efficiency of gTop-k on a cluster with 32 GPU machines which are inter-connected with 1 Gbps Ethernet. The experimental results show that our method achieves up to 2.7-12× higher scaling efficiency than S-SGD with dense gradients, and 1.1-1.7× improvement than the existing Top-k S-SGD.

    01/14/2019 ∙ by Shaohuai Shi, et al. ∙ 0 share

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  • Performance and Power Evaluation of AI Accelerators for Training Deep Learning Models

    Deep neural networks (DNNs) have become widely used in many AI applications. Yet, training a DNN requires a huge amount of calculations and it takes a long time and energy to train a satisfying model. Nowadays, many-core AI accelerators (e.g., GPUs and TPUs) play a key role in training DNNs. However, different many-core processors from different vendors perform very differently in terms of performance and power consumption. To investigate the differences among several popular off-the-shelf processors (i.e., Intel CPU, Nvidia GPU, AMD GPU and Google TPU) in training DNNs, we carry out a detailed performance and power evaluation on these processors by training multiple types of benchmark DNNs including convolutional neural networks (CNNs), recurrent neural networks (LSTM), Deep Speech and transformers. Our evaluation results make two valuable directions for end-users and vendors. For the end-users, the evaluation results provide a guide for selecting a proper accelerator for training DNN models. For the vendors, some advantage and disadvantage revealed in our evaluation results could be useful for future architecture design and software library optimization.

    09/15/2019 ∙ by Yuxin Wang, et al. ∙ 0 share

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