Efficient machine learning for mobile devices
Channel: Vienna Data Science Group
Date: 25/09/2019

Increased amount of data allows for better training and more accurate machine learning systems. Big part of generated data today is coming from embedded and mobile devices, whose number is constantly on the rise. In order to fully profit from the collected information, artificial intelligence should come to these devices. However, this is currently difficult to achieve, mostly due to the computational demands of machine learning systems being too high for constrained computational resources of mobile devices. One of the reasons for this is that, when designing machine learning methods, most people only focus on accuracy, without taking into account constrained computational resources of developed solutions.

Many applications rely on efficient machine learning. Some examples are: vision and image processing, autonomous driving, and malware detection. In order to facilitate novel applications in these domains, it is of utmost importance to provide machine learning solutions that are not just accurate, but also suitable for constrained environments. 

This talk discusses how we can design and develop such solutions suitable to be used in real-time, on-device, and at the same time customizable with respect to application requirements (accuracy, inference time, and power consumption).

Jelena Milosevic (postdoctoral researcher at the Institute of Telecommunications, Vienna University of Technology) talks at Vienna Data Science Group – Knowledgefeed vol. 29