The 5th Workshop on the Future of Computing Architectures (FOCA 2020) will be held on Friday October 30th, 2020 in a virtual format. This event is a full-day workshop that provides a forum for invited students in a broad range of fields covering all aspects of architectures for the future of computing. Invited students are expected to showcase their work and interact with their peers and members of the IBM Research community.

The topics covered by FOCA 2020 include but are not limited to:

  • Architectures for artificial intelligence / machine learning.
  • Security- and reliability-aware architectures.
  • Architectures for cloud, high-performance computing, and data centers.
  • Next-generation memory architectures.
  • Parallel architectures.
  • Power‐efficient architectures and systems.
  • Embedded, IoT, reconfigurable, and heterogeneous architectures.
  • Architectures for emerging technology and applications.
  • Quantum computing, quantum circuit optimization.

Past Editions


Organizing Committee

  • Augusto Vega
  • Karthik Swaminathan
  • Xinyu Que

Selection Committee

  • Alper Buyuktosunoglu
  • Anne Gattiker
  • Bishwaranjan Bhattacharjee
  • Bulent Abali
  • Charles Lefurgy
  • Hubertus Franke
  • Jeff Stuecheli
  • Jinjun Xiong
  • John-David Wellman
  • Kaoutar el Maghraoui
  • Leopold Grinberg
  • Manoj Kumar
  • Mihir Choudhury
  • Nagu Dhanwada
  • Nandhini Chandramoorthy
  • Ravi Nair
  • Sai Zeng
  • Sandhya Koteshwara
  • Schuyler Eldridge
  • Swagath Venkataramani
  • Talia Gershon

Invited Speakers

To be announced


To be announced


Augusto Vega is a Research Staff Member at IBM T. J. Watson Research Center involved in research and development work in the areas of highly-reliable power-efficient embedded designs, cognitive systems and mobile computing. He holds M.S. and Ph.D. degrees from Polytechnic University of Catalonia (UPC), Spain.

Karthik Swaminathan is a Research Staff Member at IBM T. J. Watson Research Center. His research interests include power-aware architectures, domain-specific accelerators and emerging device technologies in processor design. He is also interested in architectures for approximate and cognitive computing, particularly in aspects related to their reliability and energy efficiency. He holds a Ph.D. degree from Penn State University.

Xinyu Que is a Research Staff Member in the Data Centric Systems Co-Design department at the T. J. Watson Research Center. He received his M.S. degree in Computer Science and Engineering from University of Connecticut and Ph.D. degrees in Computational Science and Software Engineering from Auburn University. Xinyu has broad interests in high performance computing and large-scale graph analytics.