Download Making End Devices Play a Deeper Role in Deep Learning

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.