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Inverting the Current Cloud-centric Practice: Making End Devices Play a Deeper Role in Deep Learning Hsiang-Tsung Kung Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University Abstract At present, end devices such as mobiles, wearables and IoT nodes send their data to the cloud for processing, storage and inference; they are cut off from participating in the exciting new era of deep learning, beyond just being dumb data providers. This cloud-centric practice is unsatisfactory for certain usages due to concerns in response time, communication cost, battery consumption and privacy. It is also unfortunate business-wise because end devices give away valuable data. In this talk, we will discuss ways of making end devices take on a more active position in deep learning. We present distributed deep neural networks (DDNNs) over the cloud, edge and devices, where via joint training end devices play a critical role in allowing early decision, improving quality of inference, reducing communication cost, supporting fault tolerance and protecting data privacy, while being able to use the cloud as a fall-back to assure high-quality inference. To illustrate how much a small device can do we describe a DNN implementation for MNIST on a tiny Intel Curie wearable with only 15KB of usable memory. DDNN is joint work with Harvard graduate students, Bradley McDanel and Surat Teerapittayanon. A DDNN paper is to appear in ICDCS 2017 and a DDNN codebase is available at https://github.com/kunglab/ddnn.