June 22, 2022: Submission deadline extended!
June 9, 2022: TPC updated and submission open!
May 11, 2022: ITEM will take place on Monday, September 19 in the afternoon!
April 16, 2022: ITEM 2022 has beeen accepted at ECML-PKDD2022!
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.
Keywords: embedded machine learning, pervasive intelligent devices, real-time data analytics, uncertainty and robustness
About the Workshop
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, 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
Hardware support for novel ML architectures beyond CNNs, e.g., transformer models
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 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
Submission deadline: (was: June 23, 2022) July 8, 2022
Acceptance notification: (was: Jul 13, 2022) Aug 1, 2022
Camera-ready paper: (was: Aug 15, 2022) Aug 26, 2022
Workshop papers available online: Sept 5, 2022
Workshop date: Sept 19, 2022
Please see here for conference registration deadlines, including rules for ealy registration: https://2022.ecmlpkdd.org/index.php/registration/
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. 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.
We are negotiating with ECML-PKDD co-organizers on joint proceedings or will try to organize individual proceedings. More details to follow. In case of questions, please contact the workshop organizers. In any case, accepted papers will be posted on the workshop's website. If the authors of an accepted paper do not want to join the workshop's proceedings, these accepted papers do not preclude publishing at future conferences and/or journals.
Submission site: https://easychair.org/my/conference?conf=itemworkshop2022
Jürgen Becker, KIT
Costas Bekas, citadelsecurities
Herman Engelbrecht, University of Stellenbosch
Domenik Helms, DLR
Michael Kamps, Ruhr-University Bochum
David King, Air Force Institute of Technology
Benjamin Klenk, NVIDIA
Manfred Mücke, Materials Center Leoben
Marco Platzner, University of Paderborn
Sébastien Rumley, HES-SO Fribourg
Dolly Sapra, University of Amsterdam
Günther Schindler, SAP SE
Wei Shao, RMIT University
Yannik Stradmann, Heidelberg University
Jürgen Teich, University of Erlangen-Nuremberg
Ola Torudbakken, Graphcore
Nicolas Weber, NEC Laboratories Europe
This workshop is envisioned as a counterpart to the highly successful workshop series on embedded machine learning (WEML), held annually at Heidelberg University (for the latest edition, see https://www.deepchip.org/weml2020.). The two workshop formats complement each other, as WEML is highly discussion-oriented, invitation-only, without requirements on scientific novelty, results or publications. In contrast, ITEM is envisioned as a high-quality academic outlet, including results demonstrating at least the potential of presented work, and with a healthy mix of peer-reviewed contributions and invited talks.
Holger Fröning, Heidelberg University, Germany (holger.froening(at)ziti.uni-heidelberg.de)
Franz Pernkopf, Graz University of Technology, Austria (pernkopf(at)tugraz.at)
Michaela Blott, XILINX Research, Dublin, Ireland (michaela.blott(at)xilinx.com)
Gregor Schiele, University of Duisburg-Essen (gregor.schiele(at)uni-due.de)
Kazem Shekofteh, Heidelberg University, Germany (kazem.shekofteh(at)ziti.uni-heidelberg.de)