Euromicro Conference on
Digital System Design

August 28 – 30, 2019
Kallithea, Chalkidiki | Greece

DSD 2019

DSD 2019 Call For Papers Committees Submissions Registration

Paper Submission Deadline:

28 April 2019 (extended)

Notification of Acceptance:

3 June 2019

Camera-Ready Papers:

24 June 2019

Applications, Architectures, Methods and Tools for Machine- and Deep Learning (AMDL)

Download the call for papers in pdf format »

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 Machine Learning (ML), and particularly in Deep Learning (DL), has dramatically improved the state-of-the-art in object detection, classification and recognition, and in many other domains. Whether it is superhuman performance in object recognition or beating human players in Go, the astonishing success of DL is achieved by deep neural networks. 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. Their training requires big data sets, making the training even orders of magnitude more intensive than their already very demanding inference phase. In DSD 2019 we plan to organize several oral sessions on deep learning and related research, as well as to have invited speeches, and a poster session.

Special Session Scope

We encouraging you to submit papers related to advanced applications, architectures, methods and tools for ML and DL, especially related (but not limited) to the following topics:

  • Architectural support for ML and DL, with emphasis on energy reduction, computation efficiency and/or computation flexibility, both for inference and/or for learning
  • Spiking and brain-inspired neural networks and their implementation
  • 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
  • High-level programming language support for ML and DL
  • Advanced applications exploiting ML or DL
  • ML and DL for design automation
  • Tools and frameworks for ML and DL
  • Using of approximate computing to decrease the energy demands of ML and DL

Special Session Chairs

H. Corporaal (TU/e Eindhoven, NL)

M. Peemen (ThermoFisher Scientific, NL)

Special Session Program Committee

Henk Corporaal (TU/e Eindhoven, NL)

Nikil Dutt (Univ. California Irvine, US)

Joao C. Ferreira (Univ. Porto, PT)

Cayetano Guerra (ULPGC, ES)

Zonghua Gu (Zhejiang Univ., China)

Jim Harkin (Ulster Univ., UK)

Yifan He (XMUT, Xiamen, China)

Mario Hernandez (ULPGC, ES)

Heikki Huttunen (Tampere Univ. Techn., FI )

Jörn Janneck (TU Lund, SE)

Lech Jozwiak (TU/e Eindhoven, NL)

Ben Juurlink (TU Berlin,DE )

Achilles Kameas (Hellenic Open Univ., GR)

Georgios Keramidas (Think Silicon Ltd., GR)

Alejandro Linares-Barranco (Univ. Sevilla, ES)

Vojin G. Oklobdzija (Univ. California, US)

Maurice Peemen (Thermo Fisher Scientific, NL)

Marco Piastra (Univ. Pavia, IT)

Miroslav Skrbek (CTU Prague, CZ)

Nicolas Guil Mata (Univ. of Malaga, ES)