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首页> 外文会议>International conference on computer design >When Deep Learning Meets the Edge: Auto-Masking Deep Neural Networks for Efficient Machine Learning on Edge Devices
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When Deep Learning Meets the Edge: Auto-Masking Deep Neural Networks for Efficient Machine Learning on Edge Devices

机译：当深度学习符合边缘时：自动掩盖深度神经网络，用于边缘设备上有效的机器学习

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摘要

Deep neural network (DNN) has demonstrated promising performance in various machine learning tasks. Due to the privacy issue and the unpredictable transmission latency, inferring DNN models directly on edge devices trends the development of intelligent systems, like self-driving cars, smart Internet-of-Things (IoTs) and autonomous robotics. The on-device DNN model is obtained by expensive training via vast volumes of high-quality training data in the cloud datacenter, and then deployed into these devices, expecting it to work effectively at the edge. However, edge device always deals with low-quality images caused by compression or environmental noise pollutions. The well-trained model, though could work perfectly on the cloud, cannot adapt to these edge-specific conditions without remarkable accuracy drop. In this paper, we propose an automated strategy, called "AutoMask", to embrace effective machine learning and accelerate DNN inference on edge devices. AutoMask comprises end-to-end trainable software strategies and cost-effective hardware accelerator architecture to improve the adaptability of the device without compromising the constrained computation and storage resources. Extensive experiments, over ImageNet dataset and various state-of-the-art DNNs, show that AutoMask achieves significant inference acceleration and storage reduction while maintains comparable accuracy level on embedded Xilinx Z7020 FPGA, as well as NVIDIA Jetson TX2.
机译：深度神经网络（DNN）在各种机器学习任务中表现出有希望的性能。由于隐私问题和不可预测的传输延迟，直接在边缘设备上推断DNN模型趋势智能系统的开发，如自动驾驶汽车，智能互联网（IOT）和自主机器人。该设备上DNN模型是由昂贵的培训通过在云数据中心高质量训练数据的广阔体积获得，然后部署到这些设备，希望它在边缘有效地工作。但是，边缘设备始终处理由压缩或环境噪声污染引起的低质量图像。训练有素的模型，但可能在云上完美地工作，不能适应这些边缘特定的条件，没有显着的精度下降。在本文中，我们提出了一种被称为“Automask”的自动策略，以接受有效的机器学习并加速边缘设备的DNN推断。 Automask包含端到端的培训软件策略和经济高效的硬件加速器架构，以提高设备的适应性而不会影响受限的计算和存储资源。通过ImageNet DataSet和各种最先进的DNN进行广泛的实验，表明Automask实现了显着的推论加速和存储减少，同时保持嵌入式Xilinx Z7020 FPGA上的可比精度水平，以及NVIDIA Jetson TX2。

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