522 research outputs found
Distributed Deep Learning in the Cloud and Energy-efficient Real-time Image Processing at the Edge for Fish Segmentation in Underwater Videos Segmentation in Underwater Videos
Using big marine data to train deep learning models is not efficient, or sometimes even possible, on local computers. In this paper, we show how distributed learning in the cloud can help more efficiently process big data and train more accurate deep learning models. In addition, marine big data is usually communicated over wired networks, which if possible to deploy in the first place, are costly to maintain. Therefore, wireless communications dominantly conducted by acoustic waves in underwater sensor networks, may be considered. However, wireless communication is not feasible for big marine data due to the narrow frequency bandwidth of acoustic waves and the ambient noise. To address this problem, we propose an optimized deep learning design for low-energy and real-time image processing at the underwater edge. This leads to trading the need to transmit the large image data, for transmitting only the low-volume results that can be sent over wireless sensor networks. To demonstrate the benefits of our approaches in a real-world application, we perform fish segmentation in underwater videos and draw comparisons against conventional techniques. We show that, when underwater captured images are processed at the collection edge, 4 times speedup can be achieved compared to using a landside server. Furthermore, we demonstrate that deploying a compressed DNN at the edge can save 60% of power compared to a full DNN model. These results promise improved applications of affordable deep learning in underwater exploration, monitoring, navigation, tracking, disaster prevention, and scientific data collection projects
Structured Bayesian Compression for Deep Neural Networks Based on The Turbo-VBI Approach
With the growth of neural network size, model compression has attracted
increasing interest in recent research. As one of the most common techniques,
pruning has been studied for a long time. By exploiting the structured sparsity
of the neural network, existing methods can prune neurons instead of individual
weights. However, in most existing pruning methods, surviving neurons are
randomly connected in the neural network without any structure, and the
non-zero weights within each neuron are also randomly distributed. Such
irregular sparse structure can cause very high control overhead and irregular
memory access for the hardware and even increase the neural network
computational complexity. In this paper, we propose a three-layer hierarchical
prior to promote a more regular sparse structure during pruning. The proposed
three-layer hierarchical prior can achieve per-neuron weight-level structured
sparsity and neuron-level structured sparsity. We derive an efficient
Turbo-variational Bayesian inferencing (Turbo-VBI) algorithm to solve the
resulting model compression problem with the proposed prior. The proposed
Turbo-VBI algorithm has low complexity and can support more general priors than
existing model compression algorithms. Simulation results show that our
proposed algorithm can promote a more regular structure in the pruned neural
networks while achieving even better performance in terms of compression rate
and inferencing accuracy compared with the baselines
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