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Jointly with colleagues from Graz University of Technology, University of Duisburg-Essen, and XILINX Research, the Computing Systems Group will organize the ITEM Workshop (IoT, Edge, and Mobile for Embedded Machine Learning, https://www.item-workshop.org), collocated with ECML-PKDD2020. The goal of this workshop is to further contribute to bridging the machine learning and embedded systems domain, exploring and discussing the use of methods such as neural network compression, continuous learning, automated code generation for various embedded targets, tool-driven optimizations of mappings, future emerging technologies, new applications, and security and privacy of embedded machine learning.

Holger Fröning has been accepted as Visiting Scientist at the Chinese Academy of Sciences (CAS), respectively their President’s International Fellowship Initiative (PIFI 2020). Jointly with Bo Li from CAS Beijing, they will explore emerging materials for resistive RAM and its use for demanding applications such as embedded machine learning.

Das Institut für Technische Informatik lädt Sie herzlich zu seiner Kolloquiumsreihe "Future Computer Hardware" im Sommersemester 2020 ein. Die Vortragsreihe startet am 24.04.2020 um 15:00 Uhr mit einem Vortrag von Prof. Dr. Matthias Fertig, bis auf Weiteres zunächst im Stil eines digitalen Webinars.

Titel des Vortrags: "Innovative Computing"
Speaker: Prof. Dr. Matthias Fertig, Prof. für Computer Engineering an der Hochschule Konstanz
Abstract: PDF
Vortragssprache: Deutsch
Termin: Fr. 24.04.2020 um 15:15 via Zoom Conference

Weitere Informationen

Bitte beachten Sie die aktuellen Informationen zum Coronavirus hier.

The 3rd Workshop on Embedded Machine Learning (WEML) took place at Heidelberg University on 13 February 2020, attracting about 60 attendees. This workshop series is jointly organized by Heidelberg University (Holger Fröning), Graz University of Technology (Franz Pernkopf) and Materials Center Leoben (Manfred Mücke), and embraces joint interest in bridging the gap between complex machine learning models and methods to resource-constrained devices like edge devices, embedded devices, and the Internet of Things (IoT). The workshop focuses on invited presentations, with ample time for discussions and other interactions. This time, the program included speakers from Robert Bosch GmbH, XILINX Research, NEC Laboratories Europe, Graz University of Technology, Materials Center Leoben, and Heidelberg University. The workshop started with a focus on tooling, including an overview about resource-efficiency in deep learning, with methods such as pruning, quantization and others, followed by a deep dive into network pruning for specific hardware. Further contributions included an update on code generation for embedded targets, and an overview of machine learning optimization tools for specialized architectures. The following part focused on hardware, with presentations on using neuromorphic hardware for deep learning, and quantization-aware training for field-programmable gate arrays (FPGAs). The last part was dedicated to “Beyond-CNN” models, including Graph-based Neural Networks, Sum-Product Networks, and Capsule Networks. The attendees leveraged the workshop’s philosophy on interactions, and in various discussions a couple of trends were observed. Particularly, the community agrees on an increasing gap between ML application and hardware capability, with convolutional neural networks as a best-case scenario, as “Beyond-CNN” models will substantially push requirements in terms of structure and computational intensity. In this regard, it is also no surprise that ML and its infrastructure is trending, even though the mileage with existing tooling might vary dramatically.
 

Am 13. Februar 2020 findet der 3. Workshop für Embedded Machine Learning statt.

Weitere Informationen dazu finden Sie hier: https://www.deepchip.org/weml2020

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