Deep Learning has celebrated resounding successes in many application areas of relevance to the Internet-of-Things, for example, computer vision and machine listening. To fully harness the power of deep leaning for the IoT, these technologies must ultimately be brought directly to the edge. The obvious challenge is that deep learning techniques can only be implemented on strictly resource-constrained edge devices if the models are radically downsized. This task relies on different model compression techniques, such as network pruning, quantization, and the recent advancement of XNOR-Net. This paper examines the suitability of these techniques for audio classification on microcontrollers. We present an XNOR-Net for end-to-end raw audio classification and a comprehensive empirical study comparing this approach with pruning-and-quantization methods. We show that raw audio classification with XNOR yields comparable performance to regular full precision networks for small numbers of classes while reducing memory requirements 32-fold and computation requirements 58-fold. However, as the number of classes increases significantly, performance degrades, and pruning-and-quantization based compression techniques take over as the preferred technique being able to satisfy the same space constraints but requiring about 8x more computation. We show that these insights are consistent between raw audio classification and image classification using standard benchmark sets. To the best of our knowledge, this is the first study applying XNOR to end-to-end audio classification and evaluating it in the context of alternative techniques. All code is publicly available on GitHub.
翻译:深层次的学习在与互联网相关许多应用领域取得了巨大成功,例如计算机视觉和机器监听等。为了充分利用深度倾斜IOT的力量,这些技术最终必须直接带到边缘。显而易见的挑战是,深层次的学习技术只能在严格资源限制的边缘设备上实施,如果模型大幅缩小,那么深层次的学习技术只能在严格的资源限制的边缘设备上实施。这项任务依赖于不同的模型压缩技术,例如网络剪裁、定量化和最近XNOR-Net的升级。本文审查了这些技术在微控制器音频分类方面的适宜性。我们为最终到终端的原始音频分类提供了XNOR-Net,并将这一方法与剪裁和定量法方法进行全面的经验性研究加以比较。我们显示,与XNOR的原始音频分类的原始音频分类能够与普通的完全精准网络相比,同时减少记忆要求32倍和计算要求58倍。然而,由于课程数量大幅增加,性能下降,以及基于读音频和定量的压缩技术被作为首选的原始技术被取代,我们最喜欢的对原始技术进行精确的分类,因此需要不断进行精确地进行空间分类。我们用最精确的校正的分类。我们用最先进的技术来进行这种技术来计算。我们最精确的计算。我们最精确的计算,需要用最精确的对各种的图像进行最精确的计算。