UCLIC Research Seminar Series
In just a few short years, breakthroughs from the field of deep learning have transformed how computational models perform a wide-variety of tasks such as recognizing a face, tracking emotions or monitoring physical activities. Unfortunately, deep models and algorithms typically exert severe demands on local device resources and this conventionally limits their adoption within mobile and embedded platforms. Because sensor perception and reasoning are so fundamental to this class of computation, I believe the evolution of devices like phones, wearables and things will be crippled until we reach a point where current -- and future -- deep learning innovations can be simply and efficiently integrated into these systems.
In this talk, I will describe our progress towards developing general-purpose support for deep learning on resource-constrained mobile and embedded devices. Primarily, this requires a radical reduction in the resources (viz. energy, memory and computation) consumed by these models -- especially at inference time. I will highlight various, largely complementary, approaches we have invented to achieve this goal including: sparse layer representations, dynamic forms of compression, and scheduling partitioned model architectures. Collectively, these techniques rethink how deep learning algorithms can execute not only to better cope with mobile and embedded device conditions; but also to increase the utilization of commodity processors (e.g., DSPs, GPUs, CPUs) -- as well as emerging purpose-built deep learning accelerators.
Nic Lane holds dual academic and industrial appointments as a Senior Lecturer (Associate Professor) at University College London (UCL), and a Principal Scientist at Nokia Bell Labs. At UCL, Nic is part of the Digital Health Institute and UCL Interaction Center, while at the Bell Labs he leads DeepX -- an embedded focused deep learning unit at the Cambridge location that is part of the broader Pervasive Sensing and Systems department. Before moving to England, Nic spent four years at Microsoft Research based in Beijing. There he was a Lead Researcher within the Mobile and Sensing Systems group (MASS). 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, Nic is an experimentalist and likes to build prototype next-generation of wearable and embedded sensing devices based on well-founded computational models. His work has received multiple best paper awards, including two from ACM UbiComp (2012 and 2015). Nic's recent academic service includes serving on the PC for leading venues in his field (e.g., UbiComp, MobiSys, SenSys, WWW, CIKM), and this year he will act as PC-chair of HotMobile 2017. Nic received his PhD from Dartmouth College in 2011.