Call for Submissions

This workshop will bring classical system architecture and design experts and AI/ML algorithmic experts together in one forum. The goal is to brainstorm about challenges in designing secure and resilient AI-centric systems in general, but with a special focus on autonomous systems (such as self-driving cars and industrial robots) – where safety and security are of paramount value.

The knowledge and expertise of classical mainframe and server architects who are experts in designing ultra-reliable and secure systems will be blended with domain experts in AI; particularly those with an established expertise in developing reliable and secure AI algorithms.

The organizers of this workshop largely represent the classical system architects with expertise in building robust and energy efficient systems. The program will be a blend of talks selected from submitted abstracts and invited speakers. The latter will largely feature experts in core AI algorithms, especially those focused on adversarial robustness, few-shot learning, immunity against catastrophic forgetfulness, etc.

Call for Presentation Abstracts

This first workshop on SARA (Secure and Resilient Autonomy) will primarily focus on the safety, security and reliability aspects of AI-centric systems. Topics of interest include (but are not limited to):

  • Resilience and security considerations in emerging new domains such as autonomous vehicles, cognitive IoT swarms, as well as existing server-class and embedded architectures for the AI and machine learning applications.
  • Anomaly detection in AI-centric systems.
  • Error and threat models; associated vulnerability assessment metrics.
  • Design of secure and robust cloud-backed edge-architectures for machine learning applications.
  • Algorithmic techniques to improve training and inference in the presence of errors.
  • Hardware-software co-design of resilient machine learning systems.
  • Characterization of hardware and system-level vulnerabilities resulting in unplanned failures and/or adversarial attacks.
  • End-to-end resilience evaluation of real machine learning systems.
  • Resilient design of distributed swarm-based architectures for machine learning.
  • Lifelong learning, few-shot learning and mitigation of catastrophic forgetfulness.
  • Energy efficiency and endurance in mobile and embedded AI architectures.

Researchers in this field are encouraged to submit an extended abstract. We also encourage presentations showcasing prototype demonstrations and open source contributions.

Submission site:

If you have questions regarding submission, please contact us:

Important Dates

  • Submission of presentation abstracts: January 15th 2020 January 19th 2020 (deadline extended).
  • Notification of acceptance: January 27th 2020.
  • Final 2-page (max) paper due (for inclusion in online proceedings): February 24th 2020.
  • Workshop date: March 4th 2020.

Workshop Organization and Planning

The workshop organizing committee has put together a program committee that will assist in selecting papers (abstracts) submitted to SARA. The program committee member names will be published online within the next couple of weeks.


  1. Video recording of talks and paper proceedings will be made available on the website after the workshop.
  2. Post-workshop special issues in AI Magazine and/or IEEE Micro Magazine. (These are pending proposals, likely to be accepted).

Program Committee

To be announced.

Abstract Submission Deadline
January 19th 2020

January 27th 2020

Workshop date
March 4th 2020


To be announced


Pradip Bose is a Distinguished Research Staff Member and manager of Efficient and Resilient Systems at IBM T. J. Watson Research Center. He has over thirty-six years of experience at IBM, and was a member of the pioneering RISC super scalar project at IBM (a pre-cursor to the first RS/6000 system product). He holds a Ph.D. degree from University of Illinois at Urbana-Champaign.

Nandhini Chandramoorthy is a Research Staff Member at IBM T. J. Watson Research Center involved in Deep Neural Network ASIC design with techniques to improve reliable operation at very low supply voltages, and pre-RTL performance modeling tools for multi-core architectures. She holds a Ph.D. degree from Penn State University.

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 a Ph.D. degree 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 and resilience-aware architectures, domain-specific accelerators and emerging technologies in processor design. He is also interested in aspects related to reliability and energy efficiency, particularly in architectures for machine learning. He holds a Ph.D. degree from Penn State University.


SARA will be held in conjunction with the Third Conference on Machine Learning and Systems (MLSys). Refer to the main venue to continue with the registration process.

Event Location

Austin Convention Center
500 E Cesar Chavez St
Austin, TX 78701

Check main conference site for venue information.