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Workshop on IoT, Edge, and Mobile for Embedded Machine Learning (ITEM)/ECML-PKDD 2020

ITEM 2020

URL: https://www.item-workshop.org/
Event Date: 2020-09-14 ~ 2020-09-18
Abstract Submission Date: 2020-06-09
Submission Date: 2020-06-24
Location: Ghent, Belgium

Computer Hardware Design Computing Systems

IoT, Edge, and Mobile for Embedded Machine Learning (ITEM 2020)
https://www.item-workshop.org
In conjunction with
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2020)
https://ecmlpkdd2020.net
September 14-18, 2020, Ghent, Belgium
IMPORTANT DATES
Abstract registration deadline: June 9, 2020
Submission deadline: June 24, 2020
Acceptance notification: August 1, 2020
Camera-ready paper: August 15, 2020
Workshop program and proceedings online: September 1, 2020
Workshop date: Sept 14-18, 2020 (depending on conference organization)
ABOUT THE WORKSHOP
Local and embedded machine learning (ML) is a key component for real-time data analytics in upcoming computing environments like the Internet of Things (IoT), edge computing and mobile ubiquitous systems. The goal of the ITEM workshop is to bring together experts, researchers and practitioners from all relevant communities, including ML, hardware design and embedded systems, IoT, edge, and ubiquitous / mobile computing. Topics of interest include compression techniques for existing ML models, new ML models that are especially suitable for embedded hardware, federated learning approaches, as well as automatic code generation, frameworks and tool support. The workshop is planned as a combination of invited talks, paper submissions, as well as opentable discussions.
There is an increasing need for real-time intelligent data analytics, driven by a world of Big Data, and the society’s need for pervasive intelligent devices, such as wearables for health and recreational purposes, smart city infrastructure, e-commerce, Industry 4.0, and autonomous robots. Most applications share facts like large data volumes, real-time requirements, limited resources including processor, memory, network and possibly battery life. Data might be large but possibly incomplete and uncertain. Notably, often powerful cloud services can be unavailable, or not an option due to latency or privacy constraints. For these tasks, ML is among the most promising approaches to address learning and reasoning under uncertainty. Examples include image and speech processing, such as image recognition, segmentation, object localization, multi-channel speech enhancement, speech recognition, signal processing such as radar signal denoising, with
applications as broad as robotics, medicine, autonomous navigation, recommender systems, etc.
To address uncertainty, limited data, and to improve in general the robustness of ML, new methods are required, with examples including Bayesian approaches, sum-product networks, capsule networks, graph-based neural networks, and many more. One can observe that, compared with deep convolutional neural networks, computations can be fundamentally different, compute requirements can substantially increase, and underlying properties like structure in computation are often lost. As a result, we observe a strong need for new ML methods to address the requirements of emerging workloads deployed in the real-world, such as uncertainty, robustness, and limited data. In order to not hinder the deployment of such methods on various computing devices, and to address the gap in between application and compute hardware, we furthermore need a variety of tools.
As such, this workshop proposal gears to gather new ideas and concepts on
- ML methods for real-world deployment,
- methods for compression and related complexity reduction tools,
- dedicated hardware for emerging ML tasks,
- and associated tooling like compilers and mappers.
TOPICS OF INTEREST
Topics of particular interest include, but are not limited to:
- Compression of neural networks for inference deployment, including methods for quantization (including binarization), pruning, knowledge distillation, structural efficiency and neural architecture search
- Learning on edge devices, including federated and continuous learning
- Trading among prediction quality (accuracy), efficiency of representation (model parameters, data types for arithmetic operations and memory footprint in general), and computational efficiency (complexity of computations)
- Automatic code generation from high-level descriptions, including linear algebra and stencil codes, targeting existing and future instruction set extensions
- Tool-driven optimizations up from ML model level down to instruction level, automatically adapted to the current hardware requirements
- Understanding the difficulties and opportunities using common ML frameworks with marginally supported devices
- Exploring new ML models designed to use on designated device hardware
- Future emerging processors and technologies for use in resource-constrained environments
- Applications and experiences from deployed use cases requiring embedded ML
- New and emerging applications that require the use of ML on resource-constrained hardware
- Energy efficiency of ML models created with distinct optimization techniques
- Security/privacy of embedded ML
- New benchmarks suited to edge devices and learning on the edge scenarios
PAPER SUBMISSION GUIDELINES
Papers must be written in English and formatted according to the Springer LNCS guidelines. Author instructions, style files and the copyright form can be downloaded here: http://www.springer.com/gp/computer-science/lncs/conference-proceedings-guidelines
Submissions may not exceed 12 pages in PDF format for full papers, respectively 6 pages for short papers, including figures and references. Submitted papers must be original work that has not appeared in and is not under consideration for another conference or journal. Work in progress is welcome, but first results should be made available as a proof of concept. Submissions only consisting of a proposal will be rejected.
The workshop does not have formal proceedings, so accepted papers do not preclude publishing at future conferences and/or journals. Accepted papers will be posted on the workshop's website. The workshop co-organizers are exploring the option of co-editing a special journal issue, for which selected contributions from the workshop will be invited.
ORGANIZATION
CO-CHAIRS
Holger Fröning, Heidelberg University, Germany (holger.froening@ziti.uni-heidelberg.de)
Franz Pernkopf, Graz University of Technology, Austria (pernkopf@tugraz.at)
Gregor Schiele, University of Duisburg-Essen (gregor.schiele@uni-due.de)
Michaela Blott, XILINX Research, Dublin, Ireland (michaela.blott@xilinx.com)
TECHNICAL PROGRAM COMMITTEE
TBA
ADDITIONAL INFORMATION
https://www.item-workshop.org
or
item2020-organization@googlegroups.com

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2024-06-02 ~ 2024-06-06

2024-08-19 ~ 2024-08-22

Workshop on IoT, Edge, and Mobile for Embedded Machine Learning (ITEM)/ECML-PKDD 2020

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