Edge TPUs are a domain of accelerators for low-power, edge devices and are widely used in various Google products such as Coral and Pixel devices. In this paper, we first discuss the major microarchitectural details of Edge TPUs. Then, we extensively evaluate three classes of Edge TPUs, covering different computing ecosystems, that are either currently deployed in Google products or are the product pipeline, across 423K unique convolutional neural networks. Building upon this extensive study, we discuss critical and interpretable microarchitectural insights about the studied classes of Edge TPUs. Mainly, we discuss how Edge TPU accelerators perform across convolutional neural networks with different structures. Finally, we present our ongoing efforts in developing high-accuracy learned machine learning models to estimate the major performance metrics of accelerators such as latency and energy consumption. These learned models enable significantly faster (in the order of milliseconds) evaluations of accelerators as an alternative to time-consuming cycle-accurate simulators and establish an exciting opportunity for rapid hard-ware/software co-design.

中文翻译：

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卷积神经网络边缘TPU加速器的评估

边缘TPU是低功耗边缘设备的加速器领域，广泛用于各种Google产品（例如Coral和Pixel设备）中。在本文中，我们首先讨论Edge TPU的主要微体系结构细节。然后，我们通过423K个独特的卷积神经网络，广泛评估了三类Edge TPU，它们涵盖了当前已部署在Google产品中或产品管道中的不同计算生态系统。在此广泛研究的基础上，我们讨论有关Edge TPU类别的关键和可解释的微体系结构见解。首先，我们讨论Edge TPU加速器如何在具有不同结构的卷积神经网络中执行。最后，我们将介绍我们在开发高精度学习型机器学习模型方面正在进行的工作，以估计加速器的主要性能指标，例如延迟和能耗。这些学习的模型可以使加速器的评估速度显着提高（以毫秒为单位），以替代耗时的精确周期仿真器，并为快速的硬件/软件协同设计提供了令人兴奋的机会。