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首页> 外文期刊>Solid-State Circuits Magazine, IEEE >Embedded Deep Neural Network Processing: Algorithmic and Processor Techniques Bring Deep Learning to IoT and Edge Devices
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Embedded Deep Neural Network Processing: Algorithmic and Processor Techniques Bring Deep Learning to IoT and Edge Devices

机译：嵌入式深度神经网络处理：算法和处理器技术将深度学习带入物联网和边缘设备

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摘要

Deep learning has recently become immensely popular for image recognition, as well as for other recognition and pattern matching tasks in, e.g., speech processing, natural language processing, and so forth. The online evaluation of deep neural networks, however, comes with significant computational complexity, making it, until recently, feasible only on power-hungry server platforms in the cloud. In recent years, we see an emerging trend toward embedded processing of deep learning networks in edge devices: mobiles, wearables, and Internet of Things (IoT) nodes. This would enable us to analyze data locally in real time, which is not only favorable in terms of latency but also mitigates privacy issues. Yet evaluating the powerful but large deep neural networks with power budgets in the milliwatt or even microwatt range requires a significant improvement in processing energy efficiency.
机译：深度学习近来在图像识别以及例如语音处理，自然语言处理等中的其他识别和模式匹配任务中已变得非常流行。但是，深度神经网络的在线评估具有显着的计算复杂性，直到最近才使它仅在需要大量电能的云服务器平台上才可行。近年来，我们看到了在边缘设备（移动设备，可穿戴设备和物联网（IoT）节点）中对深度学习网络进行嵌入式处理的新兴趋势。这将使我们能够实时本地分析数据，这不仅有利于延迟，而且可以缓解隐私问题。然而，以毫瓦或什至微瓦范围内的功率预算来评估功能强大但规模较大的深度神经网络，需要在处理能效方面进行重大改进。

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