Deep Learning for Embedded Devices: The Next Step in Privacy-Preserving High-Precision Mobile Health and Wellbeing Tools
State-of-the-art models that, for example, recognize a face, track emotions, or monitor activity are increasingly based on deep learning principles. But bleeding-edge health tools, like smartphone apps and wearables, that require such user information must rely on less reliable learning methods to locally process data because of the excessive device resources demanded by deep models. In this talk, I will describe our research that drives towards a complete rethinking of how existing forms of deep learning executes at inference time on embedded health platforms. Not only does this cause radically lower energy, computation and memory requirements; it also significantly increases the utilization of commodity processors (e.g., GPUs, CPUs) -- and even emerging purpose-built hardware, when available.
Nic Lane is a Principal Scientist at Bell Labs where he is a member of the Internet of Things research group. Before joining Bell Labs, he spent four years as a Lead Researcher at Microsoft Research based in Beijing. Nic received his Ph.D. from Dartmouth College (2011), his dissertation pioneered community-guided techniques for learning models of human behavior. These algorithms enable mobile sensing systems to better cope with diverse user populations and conditions routinely encountered in the real-world. More broadly, Nic's research interests revolve around the systems and modeling challenges that arise when computers collect and reason about people-centric sensor data. At heart, he is an experimentalist who likes to build prototype sensing systems based on well-founded computational models.