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.