Exploiting Energy-Accuracy Trade-off through Contextual Awareness in Multi-Stage Convolutional Neural Networks
Exploiting Energy-Accuracy Trade-off through Contextual Awareness in Multi-Stage Convolutional Neural Networks
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DOI：
10.1109/isqed.2019.8697497
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发表时间：
2019-03
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影响因子：
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通讯作者：
Katayoun Neshatpour;F. Behnia;H. Homayoun;Avesta Sasan
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文献类型：
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作者：
Katayoun Neshatpour;F. Behnia;H. Homayoun;Avesta Sasan
节能CNN的一个有前途的解决方案是将它们分解为顺序执行的多个阶段（MS-CNN）。在本文中，我们说明了与深度CNN不同，MS-CNN在初始阶段开发了一种输入数据的上下文感知形式，可用于动态改变此类网络的结构和连接性，以降低其计算复杂度，使其更适合低功耗和实时系统。我们提出了三个运行时的优化政策，这是能够探索这样的上下文知识，并说明所提出的政策如何构建一个动态的体系结构，适用于广泛的应用程序具有不同的精度要求，资源和时间预算，而不需要进一步的网络重新训练。此外，我们提出了可变和动态位长定点转换，以进一步减少MS-CNN的内存占用。
One of the promising solutions for energy-efficient CNNs is to break them down into multiple stages that are executed sequentially (MS-CNN). In this paper, we illustrate that unlike deep CNNs, MS-CNNs develop a form of contextual awareness of input data in initial stages, which could be used to dynamically change the structure and connectivity of such networks to reduce their computational complexity, making them a better fit for low-power and real-time systems. We suggest three run-time optimization policies, which are capable of exploring such contextual knowledge, and illustrate how the proposed policies construct a dynamic architecture suitable for a wide range of applications with varied accuracy requirements, resources, and time-budget, without further need for network re-training. Moreover, we propose variable and dynamic bit-length fixed-point conversion to further reduce the memory footprint of the MS-CNNs.