Machine Learning Systems: On-device AI and beyond

Lecturer: Nic Lane

Event type: Guest lecture

Event time: 2018-02-27 15:15 to 16:30

Place: Small Hall, Main Building, University of Helsinki, Fabianinkatu 33, Helsinki

Web page: Helsinki Distinguished Lecture Series on Future IT


The next lecture in the Helsinki Distinguished Lecture Series on Future Information Technology will be given by Professor Nic Lane from the University of Oxford.

The lecture is free of charge and open to everyone interested in the latest research in information technology. The lecture will be followed by an informal cocktail event.

Please register here.


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, driving a car or the translation of a language. 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: binary “on-the-fly” networks, 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.

About the Speaker

Nic Lane is an Associate Professor in the Computer Science Department at the University of Oxford. Before joining Oxford, he held dual appointments at University College London (UCL) and Nokia Bell Labs; at Nokia, as a Principal Scientist, Nic founded and led DeepX – an embedded focused deep learning unit at the Cambridge location. Nic specializes in the study of efficient machine learning under computational constraints, and over the last three years has pioneered a range of embedded and mobile forms of deep learning. This work formed the basis for his 2017 Google Faculty Award in machine learning. Nic’s work has received multiple best paper awards, including ACM/IEEE IPSN 2017 and two from ACM UbiComp (2012 and 2015). This year he will serve as the PC Chair of ACM SenSys 2018. Prior to moving to England, Nic spent four years at Microsoft Research based in Beijing as a Lead Researcher. He received his PhD from Dartmouth College in 2011. More information about Nic is available from