Workshop on Compute Platforms for Autonomous Vehicles

To be held along with 55th IEEE/ACM International Symposium on Microarchitecture (MICRO'22)

October 1, 2022

Program

  • 8:00 AM - 8:05 AM Introduction

  • 8:05 AM - 8:40 AM Safety of the Intended Functionality (SOTIF) for Autonomous Driving - Shaoshan Liu (Perceptin) <slides>

  • 8:40 AM - 9:25 AM Autonomous Driving and its Computing Problems with Autoware - Shinpei Kato (University of Tokyo, Autoware)

  • 9:25 AM - 9:50 AM Sliding Window Support for Image Processing in Autonomous Vehicles - Raul Taranco, Jose Maria Arnau, Antonio Gonzalez (UPC) <paper> <slides>

  • 9:50 AM - 10:25 AM Break

  • 10:25 AM - 11:10 AM Towards a Benchmark for Automotive Computing - Rod Shojaei (UC Davis) <slides>

  • 11:10 AM - 11:55 AM Enabling the Software-Defined AV Revolution Through Accelerated Computing and AI - Tom Tomazin (NVIDIA)

  • 11:55 AM - 12:00 PM Conclusion

About the Workshop

The global market for autonomous vehicles is expected to surpass $300 billion by the end of the decade. Due to the unique requirements of simultaneously providing sufficient computational power to run the necessary workloads needed to support autonomous driving, achieving the efficiency necessary to run the system in the resource-constrained environment of an on-road vehicle, and ensuring reliable execution, specialized compute platforms are required for this domain.

We welcome submissions focused on compute platforms for autonomous vehicles (cars/drones/robots). Key topics that we seek to address in the workshop include:

  • Energy-efficient machine learning inference accelerators

  • Techniques for exploiting reduced precision or sparsity in hardware

  • Techniques for improving energy efficiency of hardware

  • Techniques for maintaining operation/performance in an expanded thermal range

  • Techniques to ensure deterministic performance

  • Reliability and safety considerations

Call for Papers

We solicit both full papers (8-10 pages), as well as short/position papers (2-4 pages). Submissions are not double-blind (author names must be included). The page limit includes figures, tables, and appendices, but excludes references. Please use standard IEEE Word or LaTex templates. All submissions will need to be made via EasyChair.

Each submission will be reviewed by at least three reviewers from the program committee. Papers will be reviewed for novelty, quality, technical strength, and relevance to the workshop. All accepted papers will be made available online.


Please direct any questions to tomstjohn617@gmail.com


Submission Deadline: 9/2/2022

Acceptance Notification: 9/12/2022

Camera-Ready Version Due: 9/23/2022

Program Committee

  • Amin Farmahini (Cruise)

  • Michael Goldfarb (Encharge AI)

  • Armin Runge (Bosch)

  • Ken Shiring (SiMa)

Organizers

Tom St. John

Tom St. John is a senior software engineer at Cruise, where he serves as a technical lead for the Compute Platforms Group. Prior to his current role, he led the distributed machine learning performance optimization efforts within Tesla Autopilot and served as the director of the AI Co-Design Center at Wave Computing. His research primarily focuses on the intersection of parallel programming models and computer architecture design, and the impact that this has on large-scale machine learning.

Vijay Janapa Reddi

Vijay Janapa Reddi is an Associate Professor in John A. Paulson School of Engineering and Applied Sciences at Harvard University. Prior to joining Harvard University, he was a an Associate Professor at The University of Texas at Austin where he continues to be an Adjunct Professor. He leads the MLPerf inference benchmarking effort. His research interests include computer architecture, compilers and runtime systems, specifically in the context of mobile and edge computing systems to improve their performance, power efficiency, and reliability.