DeepAI
Log In Sign Up

On-Device Machine Learning: An Algorithms and Learning Theory Perspective

11/02/2019
by   Sauptik Dhar, et al.
0

The current paradigm for using machine learning models on a device is to train a model in the cloud and perform inference using the trained model on the device. However, with the increasing number of smart devices and improved hardware, there is interest in performing model training on the device. Given this surge in interest, a comprehensive survey of the field from a device-agnostic perspective sets the stage for both understanding the state-of-the-art and for identifying open challenges and future avenues of research. Since on-device learning is an expansive field with connections to a large number of related topics in AI and machine learning (including online learning, model adaptation, one/few-shot learning, etc), covering such a large number of topics in a single survey is impractical. Instead, this survey finds a middle ground by reformulating the problem of on-device learning as resource constrained learning where the resources are compute and memory. This reformulation allows tools, techniques, and algorithms from a wide variety of research areas to be compared equitably. In addition to summarizing the state of the art, the survey also identifies a number of challenges and next steps for both the algorithmic and theoretical aspects of on-device learning.

READ FULL TEXT

page 1

page 2

page 3

page 4

12/01/2022

On-device Training: A First Overview on Existing Systems

The recent breakthroughs in machine learning (ML) and deep learning (DL)...
12/10/2012

Online Portfolio Selection: A Survey

Online portfolio selection is a fundamental problem in computational fin...
03/29/2017

The Top 10 Topics in Machine Learning Revisited: A Quantitative Meta-Study

Which topics of machine learning are most commonly addressed in research...
02/02/2021

It's always personal: Using Early Exits for Efficient On-Device CNN Personalisation

On-device machine learning is becoming a reality thanks to the availabil...
02/01/2022

Identifying Pauli spin blockade using deep learning

Pauli spin blockade (PSB) can be employed as a great resource for spin q...
05/10/2021

GSPMD: General and Scalable Parallelization for ML Computation Graphs

We present GSPMD, an automatic, compiler-based parallelization system fo...