Luca Benini from ETHZ and Bologna University just confirmed to be the second keynote speaker!
Submission site is open!
We are happy to announce Song Han from MIT as one of your keynote speakers!
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.
Keywords: embedded machine learning, pervasive intelligent devices, real-time data analytics, uncertainty and robustness
Details on Intersection of Machine Learning and Computer Architecture
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. Application examples include wearables for health and recreational purposes, infrastructure such as smart cities, transportation and smart power grids, e-commerce and Industry 4.0, and autonomous robots including self-driving cars. Most applications share facts like large data volumes, real-time requirements, limited resources including processor, memory and network. Often, battery life is a concern, data might be large but possibly incomplete, and probably most important, data can be 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. In particular deep learning methods in general are well-established supervised or unsupervised ML methods, and well understood with regard to compute/data requirements, accuracy and (partly) generalization. Today’s deep learning algorithms dramatically advance state-of-the-art performance in terms of accuracy of the vast majority of AI tasks. 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.
As a result, ML is embedded in various compute devices, ranging from power cloud systems over fog and edge computing to smart devices. Due to the demanding nature of this workload, which is heavily compute- and memory-intensive, virtually all deployments are limited by resources, being particularly true for IoT, edge, and mobile. One of the results of these constraints are various specialized processor architectures, which are tailored for particular ML tasks. While this is helpful for this particular task, ML is advancing fast and new methods are introduced frequently. Notably, one can observe that very often the requirements of such tasks advance faster than the performance of new compute hardware, increasing the gap in between application and compute hardware. This observation is emphasized by the slowing-down of Moore’s law, which used to deliver constant performance scaling over decades.
Furthermore, 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.
Similarly, the workshop also gears to serve as a platform that gathers experts from ML and systems for joint tackling of these problems, creating an atmosphere of open discussions and other interactions.
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
Important Dates (updated)
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, 2020
Please see here for conference registration deadlines, including rules for ealy registration: https://ecmlpkdd2020.net/attending/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. 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.
Submission site: https://easychair.org/conferences/?conf=item2020
Costas Bekas, Citadel Securites
Herman Engelbrecht, Stellenbosch University, South Africa
Giulio Gambardella, XILINX
Tobias Golling, Geneva University, Switzerland
Domenik Helms, OFFIS e.V. - Institut für Informatik, Germany
Eduardo Rocha Rodrigues, IBM
David King, Air Force Institute of Technology, USA
Benjamin Klenk, NVIDIA Research
Manfred Mücke, Materials Center Leoben Forschung GmbH, Austria
Dimitrios S. Nikolopoulos, Virginia Tech, USA
Robert Peharz, University of Technology Eindhoven, NL
Marco Platzner, Paderborn University, Germany
Thomas B. Preußer, ETH Zurich, Switzerland
Johannes Schemmel, Heidelberg University, Germany
Wei Shao, Royal Melbourne Institute of Technology (RMIT), Australia
David Sidler, Microsoft
Jürgen Teich, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Germany
Sebastian Tschiatschek, University of Vienna, Austria
Nicolas Weber, NEC Labs Europe
Matthias Zöhrer, Evolve.tech, Austria
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)
Gregor Schiele, University of Duisburg-Essen (gregor.schiele(at)uni-due.de)
Michaela Blott, XILINX Research, Dublin, Ireland (michaela.blott(at)xilinx.com)