[hpc-announce] CfP: 5th Workshop on IoT, Edge, and Mobile for Embedded Machine Learning (ITEM)/ECML-PKDD 2024

Holger Fröning holger.froening at ziti.uni-heidelberg.de
Mon May 13 04:38:07 CDT 2024 

(apologies if you receive multiple publicity emails)

(Submission deadline: June 15, 2024)

(Update on TPC and submission site)

===============================================================================

                           CALL FOR PAPERS

                     5th International Workshop on 
         IoT, Edge, and Mobile for Embedded Machine Learning
                             (ITEM 2024)

                    https://urldefense.us/v3/__https://www.item-workshop.org__;!!G_uCfscf7eWS!a6jmoLe2ICIEmfAg4sfRsB1nvnV06ssU05BQsm8jxOrA2A73KCiveB2airh4uhzgVs_AuwqqCk0l-6oIAuouIFDU1kCu2UrFomGWdG5ouOJzERg$                                    

                 September 9-13 (TBD), 2024, Vilnius, Lithuania

                         In conjunction with
               European Conference on Machine Learning
                                 and
     Principles and Practice of Knowledge Discovery in Databases

                           (ECML-PKDD 2024)

                       https://urldefense.us/v3/__https://2024.ecmlpkdd.org__;!!G_uCfscf7eWS!a6jmoLe2ICIEmfAg4sfRsB1nvnV06ssU05BQsm8jxOrA2A73KCiveB2airh4uhzgVs_AuwqqCk0l-6oIAuouIFDU1kCu2UrFomGWdG5oLNvIRz0$ 

===============================================================================

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, tractable models beyond neural networks, 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 open-table 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, Machine Learning (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, transformer networks, graph-based neural networks, deep
ensembles 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 methods to address the
requirements of emerging ML applications 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 addresses the complete vertical stack of ML deployment,
and as such gears to gather new ideas and concepts on 

- ML methods for real-world deployment, 
- methods for compression and related complexity reduction tools, 
- associated tooling like compilers and mappers,
- and dedicated hardware for emerging ML applications


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
- Hardware support for novel ML architectures beyond CNNs, e.g., transformers
- Tractable models beyond neural networks
- 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, e.g., RISC V, embedded FPGAs, or analogue technologies
- 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 and embedded devices

IMPORTANT DATES

Workshop paper submission deadline: June 15, 2024
Workshop Paper Author Notification: July 15, 2024
Camera-ready deadline: approx. July 31, 2024 (depending on ECML-PKDD) 
Workshop date: full day, in between Sept 9 - 13, 2024 (depending on ECML-PKDD)


PAPER SUBMISSION GUIDELINES

Papers must be written in English and formatted according to the Springer LNCS 
guidelines. An author kit can be found here: 
https://urldefense.us/v3/__https://klevas.mif.vu.lt/*linp/ecmlpkdd2024/ECML_PKDD_2024_Author_Kit.zip__;fg!!G_uCfscf7eWS!a6jmoLe2ICIEmfAg4sfRsB1nvnV06ssU05BQsm8jxOrA2A73KCiveB2airh4uhzgVs_AuwqqCk0l-6oIAuouIFDU1kCu2UrFomGWdG5oh5KMMJY$ 

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. Please prepare submissions according to
single-blind standards. 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. 

ECML-PKDD2024 will organize joint post-workshop proceeding published by 
Springer Communications in Computer and Information Science, in 1-2 volumes,
organized by focused scope and possibly indexed by WOS. Papers authors will
have the faculty to opt-in or opt-out. Online submission: 
https://urldefense.us/v3/__https://cmt3.research.microsoft.com/ECMLPKDDWorkshops2024__;!!G_uCfscf7eWS!a6jmoLe2ICIEmfAg4sfRsB1nvnV06ssU05BQsm8jxOrA2A73KCiveB2airh4uhzgVs_AuwqqCk0l-6oIAuouIFDU1kCu2UrFomGWdG5oJR13REM$  
(please ensure you choose the "ITEM" track)


ORGANIZATION

CO-CHAIRS
- Holger Fröning, Heidelberg University, Germany 
  (holger.froening at ziti.uni-heidelberg.de) 
- Gregor Schiele, University of Duisburg-Essen
  (gregor.schiele at uni-due.de)
- Franz Pernkopf, Graz University of Technology, Austria
  (pernkopf at tugraz.at)
- Michaela Blott, AMD / XILINX Research, Dublin, Ireland
  (michaela.blott at amd.com)


TECHNICAL PROGRAM COMMITTEE
Dolly Sapra, University of Amsterdam, Netherlands
Masoud Daneshtal, Mälardalen University, Sweden
Jose Cano Reyes, University of Glasgow, UK
Manuel Dolz, Universitat Jaume I, Spain
Axel Jantsch, TU Vienna, Austria
Sébastien Rumley, University of Applied Sciences Western Switzerland
Jürgen Teich, University of Erlangen-Nuremberg, Germany
Enrique Quintana-Ortí, Technical University of Valencia, Spain
Pinar Tözün, IT University of Copenhagen, Denmark
Mattias O'Nils, Mittuniversitetet, Sweden
Manuele Rusci, KU Leuven, Belgium
Arya Mazaheri, TU Darmstadt, Germany
Grace Zhang, TU Darmstadt, Germany
Martin Andraud, UC Louvain, Belgium
Herman Engelbrecht, University of Stellenbosch, South Africa
Diana Goehringer, TU Dresden, Germany
Jürgen Becker, KIT, Germany
Nicolas Weber, NEC Laboratories Europe, Germany
Burghard Ringlein, IBM Zurich, Switzerland



ADDITIONAL INFORMATION

https://urldefense.us/v3/__https://www.item-workshop.org__;!!G_uCfscf7eWS!a6jmoLe2ICIEmfAg4sfRsB1nvnV06ssU05BQsm8jxOrA2A73KCiveB2airh4uhzgVs_AuwqqCk0l-6oIAuouIFDU1kCu2UrFomGWdG5ouOJzERg$

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