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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：当深度学习遇到边缘时：自动掩盖深度神经网络以在边缘设备上进行高效的机器学习

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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已展示出令人鼓舞的性能。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模型将推动智能系统的发展，例如自动驾驶汽车，智能物联网（IoT）和自动机器人。设备上的DNN模型是通过在云数据中心中通过大量高质量的培训数据进行昂贵的培训而获得的，然后部署到这些设备中，期望它可以在边缘有效地工作。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。训练有素的模型尽管可以在云上完美运行，但如果没有明显的精度下降，就无法适应这些特定于边缘的条件。在本文中，我们提出了一种称为“ AutoMask”的自动化策略，以包含有效的机器学习并加速边缘设备上的DNN推理。 AutoMask包括端到端的可训练软件策略和经济高效的硬件加速器体系结构，可在不损害受约束的计算和存储资源的情况下提高设备的适应性。在ImageNet数据集和各种最新的DNN上进行的大量实验表明，AutoMask在嵌入式Xilinx Z7020 FPGA和NVIDIA Jetson TX2上可实现显着的推理加速和存储减少，同时保持相当的精度水平。

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