Hardware-Software Co-Design is a highly successful strategy for improving performance of domain-specific computing systems. We argue for the application of the same methodology to deep learning; specifically, we propose to extend neural architecture search with information about the hardware to ensure that the model designs produced are highly efficient in addition to the typical criteria around accuracy. Using the task of keyword spotting in audio on edge computing devices, we demonstrate that our approach results in neural architecture that is not only highly accurate, but also efficiently mapped to the computing platform which will perform the inference. Using our modified neural architecture search, we demonstrate $0.88\%$ increase in TOP-1 accuracy with $1.85\times$ reduction in latency for keyword spotting in audio on an embedded SoC, and $1.59\times$ on a high-end GPU.

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面向性能的神经架构搜索

硬件-软件协同设计是提高特定领域计算系统性能的一种非常成功的策略。我们主张将相同的方法应用于深度学习；具体来说，我们建议使用有关硬件的信息扩展神经架构搜索，以确保所生成的模型设计除了围绕准确性的典型标准之外还具有高效性。使用边缘计算设备上音频中的关键字发现任务，我们证明了我们的方法产生的神经架构不仅高度准确，而且还有效地映射到将执行推理的计算平台。使用我们改进的神经架构搜索，我们证明了 TOP-1 准确度提高了 0.88 美元，嵌入式 SoC 上音频中的关键字发现延迟减少了 1.85 美元，