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From Andrew Palumbo <andrew_palu...@msn.com>
Subject Fwd: CFP: IEEE Computer Magazine -- Special Issue on Mobile and Embedded Deep Learning
Date Sat, 26 Aug 2017 23:38:52 GMT

Fyi @here


Sent from my Verizon Wireless 4G LTE smartphone


-------- Original message --------
From: Nic Lane <niclane@acm.org>
Date: 08/26/2017 3:33 AM (GMT-08:00)
To: Nic Lane <niclane@acm.org>
Subject: CFP: IEEE Computer Magazine -- Special Issue on Mobile and Embedded Deep Learning

Hi folks, please consider submitting to the upcoming IEEE Computer Magazine special issue
on mobile and embedded forms of deep learning: https://www.computer.org/computer-magazine/2017/07/10/mobile-and-embedded-deep-learning-call-for-papers/

Below you will find the complete CFP. the submission deadline is Sept 15. Please mail us with
any questions.

Best,

Nic Lane (UCL and Nokia Bell Labs) and Pete Warden (Google Brain)
Special Issue Guest Editors -- IEEE Computer Magazine

===

Call for Papers: IEEE Computer Magazine -- Special Issue on Mobile and Embedded Deep Learning

DEADLINE (EXTENDED): 15 September 2017
Publication date: April 2018 (with recommended early release on arxiv.org<http://arxiv.org/>)

https://www.computer.org/computer-magazine/2017/07/10/mobile-and-embedded-deep-learning-call-for-papers/

In recent years, breakthroughs from the field of deep learning have transformed how sensor
data from cameras, microphones, and even accelerometers, LIDAR, and GPS can be analyzed to
extract the high-level information needed by the increasingly commonplace examples of sensor-driven
systems that range from smartphone apps and wearable devices to drones, robots, and autonomous
cars.

Today, the state of the art in computational models that, for example, recognize a face in
a crowd, translate one language into another, discriminate between a pedestrian and a stop
sign, or monitor the physical activities of a user, are increasingly based on deep-learning
principles and algorithms. Unfortunately, deep-learning models typically exert severe demands
on local device resources, which typically limits their adoption in mobile and embedded platforms.
As a result, in far too many cases, existing systems process sensor data with machine learning
methods that were superseded by deep learning years ago.

Because the robustness and quality of sensory perception and reasoning is so critical to mobile
and embedded computing, we must begin the careful work of addressing two core technical questions.
First, to ensure that the sensor-inference problems that are central to this class of computing
are adequately addressed, how should existing deep-learning techniques be applied and new
forms of deep learning be developed? Meeting this challenge involves a combination of learning
applications—some of which are familiar to other domains (such as in processing image and
audio), as well as those more uniquely tied to wearable and mobile systems (such as activity
recognition). Second, for the compute, memory, and energy overhead of current—and future—deep-learning
innovations, what will be required to improve efficiency and effectively integrate into a
variety of resource-constrained platforms? Solutions to such efficiency challenges will come
from innovations in algorithms, systems software, and hardware (such as in ML-accelerators
and changes to conventional processors).

In this special issue of Computer, the guest editors aim to consider these two broad themes,
which drive further advances in mobile and embedded deep learning. More specific topics of
interest include but are not limited to

= Compression of deep model architectures;
= Neural-based approaches for modeling user activities and behavior;
= Quantized and low-precision neural networks (including binary networks);
= Mobile vision supported by convolutional and deep networks;
= Optimizing commodity processors (GPUs, DSPs etc.) for deep models;
= Audio analysis and understanding through recurrent and deep architectures;
= Hardware accelerators for deep neural networks;
= Distributed deep model training approaches;
= Applications of deep neural networks with real-time requirements;
= Deep models of speech and dialog interaction or mobile devices; and
= Partitioned networks for improved cloud- and processor-offloading.

SUBMISSION DETAILS

Only submissions that describe previously unpublished, original, state-of-the-art research
and that are not currently under review by a conference or journal will be considered.

There is a strict 6,000-word limit (figures and tables are equivalent to 300 words each) for
final manuscripts. Authors should be aware that Computer cannot accept or process papers that
exceed this word limit.

Articles should be understandable by a broad audience of computer science and engineering
professionals, avoiding a focus on theory, mathematics, jargon, and abstract concepts.

All manuscripts are subject to peer review on both technical merit and relevance to Computer’s
readership. Accepted papers will be professionally edited for content and style. For accepted
papers, authors will be required to provide electronic files for each figure according to
the following guidelines: for graphs and charts, authors must submit them in their original
editable source format (PDF, Visio, Excel, Word, PowerPoint, etc.); for screenshots or photographs,
authors must submit high-resolution files (300 dpi or higher at the largest possible dimensions)
in JPEG or TIFF formats.

Authors of accepted papers are encouraged to submit multimedia, such as a 2- to 4-minute podcast,
videos, or an audio or audio/video interview of the authors by an expert in the field, which
Computer staff can help facilitate, record, and edit.

For author guidelines and information on how to submit a manuscript electronically, visit
www.computer.org/web/peer-review/magazines<http://www.computer.org/web/peer-review/magazines>.
For full paper submission, please visit mc.manuscriptcentral.com/com-cs<http://mc.manuscriptcentral.com/com-cs>.

QUESTIONS?

Please direct any correspondence before submission to the guest editors:

Nic Lane, University College London and Nokia Bell Labs (nic.lane@cs.ucl.ac.uk<mailto:nic.lane@cs.ucl.ac.uk>)

Pete Warden, Google Brain (petewarden@google.com<mailto:petewarden@google.com>)
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