[hpc-announce] CfP: Workshop on IoT, Edge, and Mobile for Embedded Machine Learning (ITEM)/ECML-PKDD 2023

Holger Fröning holger.froening at ziti.uni-heidelberg.de
Thu Jun 8 11:50:17 CDT 2023

(apologies if you receive multiple publicity emails)

(Extended submission deadline: June 26, 2023)


                           CALL FOR PAPERS

                     4th International Workshop on 
         IoT, Edge, and Mobile for Embedded Machine Learning
                             (ITEM 2023)


                    September 18 or 22, 2023, Torino, Italy

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

                           (ECML-PKDD 2023)




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, 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 

- 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 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 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: June 26, 2023 (extended from June 12, firm)
Acceptance notification: July 12, 2023
Camera-ready paper: TBD
Workshop date: Sept 18 or 22, 2023 (depending on conference organization)


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: 

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-PKDD2023 will organize joint workshop proceedings including papers accepted
at ITEM. 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.


- Gregor Schiele, University of Duisburg-Essen (gregor.schiele at uni-due.de)
- 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, AMD / XILINX Research, Dublin, Ireland (michaela.blott(at)amd.com)




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