Gregor Schiele, University of Duisburg-Essen, Germany (gregor.schiele (at) uni-due.de)
Holger Fröning, ZITI, Heidelberg University, Germany (holger.froening (at) ziti.uni-heidelberg.de)
Franz Pernkopf, Graz University of Technology, Austria (pernkopf (at) tugraz.at)
Manfred Mücke, Materials Center Leoben GmbH, Leoben, Austria (Manfred.Muecke (at) mcl.at)
The workshop series on embedded machine learning (WEML) is jointly organized by Heidelberg University, University of Duisburg-Essen, Graz University of Technology, and Materials Center Leoben, and embraces our joint interest in bringing complex machine learning models and methods to resource-constrained devices like edge devices, embedded devices, and Internet of Things (IoT). The workshop is rather informal, without proceedings, and is organized around a set of invited talks on topics associated with this interest.
With embedded we refer to embedded in the real-world, thus operating under conditions that include
Limited resources in terms of compute, memory and power consumption
Noisy and incomplete training data, as well as noisy sensor data
Part of a complex system architecture
Topics of interest include in general:
Compression of neural networks for inference deployment, including methods for quantization, pruning, knowledge distillation, structural efficiency and neural architecture search
Hardware support for emerging neural architectures beyond the state-of-the-art
Tractable models beyond neural networks
Reasoning about uncertainty
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
New and emerging applications that require ML on resource-constrained hardware
Security/privacy of embedded ML
New benchmarks suited to edge and embedded devices
In this regard, the workshop gears to gather experts from various domains and from both academia and industry, to stimulate discussions on recent advances in this area.
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