Applications, Architectures, Methods and Tools for Machine – and Deep Learning is a Special Sessions at the 26th Euromicro Conference Digital System Design (DSD) conference.

Most important issues (more details below).

• scope: advanced applications, architectures, design methods and tools, and system software for artificial intelligence, machine learning and deep learning
• submission deadline: April 17th, 2023
• conference: Sept. 6th – Sept. 8th, 2023, Durres, Albania
• contact: Tomasz Kryjak (tomasz.kryjak@agh.edu.pl), Niki Martinel (niki.martinel@uniud.it), Ercan Kalali (e.kalali@tue.nl)

Applications, Architectures, Methods and Tools for Machine – and Deep Learning (AAMTM) – https://dsd-seaa2023.com/aamtm/

Machine learning has numerous important applications in intelligent systems within many areas, like automotive, avionics, robotics, health-care, well-being, and security. The recent progress in Artificial Intelligence (AI), and particularly in Deep Learning (DL) / Machine Learning (ML), has dramatically improved the state-of-the-art in object detection, classification and recognition, natural language processing, games, medical imaging, etc. However, the complexity of DL-networks for many practical applications can be huge, and their processing may demand a high computing effort and excessive energy consumption. This can become a gigantic challenge when considering embedded inference implementation for Smart Cyber Physical Systems (like autonomous vehicles and robotics) and Internet-of-Things (like healthcare-IoT and predictive maintenance for Industry 4.0). Moreover, even training of such complex DL-networks over massive data sets is triggering new avenues in training accelerator design. In DSD 2023, we plan to organize several oral sessions on embedded deep learning/AI and related research, as well as to have invited speeches, and a poster session.
Special Session Scope

We welcome submissions related to advanced applications, architectures, design methods and tools, and system software for AI, ML and DL, especially related (but not limited) to the following topics:

• Architectures for ML and DL, with emphasis on energy reduction, computation efficiency and/or computation flexibility, both for inference and/or for learning
• Neuromorphic architectures, Spiking and brain-inspired neural networks and their implementation
• Efficient Edge Computing for ML and DL
• Efficient mapping of ML and DL applications to target architectures, including many-core, GPGPU, SIMD, FPGA, and HW accelerators
• New learning approaches for ML and DL, with emphasis on e.g. faster and more efficient learning, online learning, and quality of learning, training accelerators, etc.
• High-level programming language support for ML and DL
• Advanced applications exploiting ML or DL
• ML and DL for design automation
• Tools, frameworks, and system software for ML and DL
• Using of approximate computing to decrease the energy demands of ML and DL
• Security and Reliability issues for ML and DL, for both inference and training

Submission Guidelines

Authors are encouraged to submit their manuscripts via EasyChair web service at web page https://easychair.org/conferences/?conf=dsd2023. Each manuscript should include the complete paper text, all illustrations, and references. The manuscript should conform to the IEEE format: single-spaced, double column, US letter page size, 10-point size Times Roman font, up to 8 pages. In order to conduct a blind review, no indication of the authors’ names should appear in the manuscript, references included.

Special Session Chairs

• Tomasz Kryjak (AGH University of Science and Technology in Krakow, Poland)
• Niki Martinel (University Udine, Italy)
• Ercan Kalali (TU Eindhoven, The Netherlands)

Special Session Program Committee

• Alberto Marchisio (Vienna University of Technology)
• Alessandro Capotondi (University of Modena and Reggio-Emilia, Italy)
• Alfonso Rodriguez (Universidad Politécnica de Madrid, Spain)
• Antonio Carlos Schneider Beck (Universidade Federal do Rio Grande do Sul, Brazil)
• Anuj Pathania (Universiteit van Amsterdam)
• Christian Pilato (Politecnico di Milano)
• Emanuele Torti (University of Pavia)
• Georgios Zervakis (Karlsruhe Institute of Technology)
• Ghayoor Gillani (University of Twente)
• Ihsen Alouani (IEMN-DOAE/UMR CNRS Polytechnic University Hauts-de-France)
• Jani Boutellier (University of Vaasa, Finland)
• José Cano (University of Glasgow, United Kingdom)
• Karol Desnos (Univeristy Rennes, INSA Rennes)
• Mateusz Komorkiewicz (IEEE: VTS Chapter, Poland)
• Maurizio Martina (Politecnico di Torino)
• Miguel Chavarrías (Universidad Politécnica de Madrid, Spain)
• Miguel Figueroa (Universidad de Concepcion, Chile)
• Muhammad Abdullah Hanif (New York University Abu Dhabi)
• Pierre Langlois (Ecole Polytechnique de Montreal)
• Satwik Patnaik (Texas A&M University)
• Tiago Dias (Instituto Superior de Engenharia de Lisboa, Portugal)
• Vojtech Mrazek (Brno University of Technology)

Contact Information

• Tomasz Kryjak – tomasz.kryjak@agh.edu.pl
• Niki Martinel – niki.martinel@uniud.it
• Ercan Kalali – e.kalali@tue.nl