AVCC's latest technical report "TR-004 Models and Datasets for Benchmarking Deep Neural Networks for Automated and Assisted Driving Systems" is now available to the public for free download.
SAN JOSE, Calif., Jan. 11, 2023 /PRNewswire-PRWeb/ -- AVCC, a global consortium of automotive and technology industry leaders cooperating on intelligent software-defined and automated vehicle technology, today announced that its latest technical report is available to the public for free download: "TR-004 Models and Datasets for Benchmarking Deep Neural Networks for Automated and Assisted Driving Systems."
"There was no alignment in the industry on how to fairly compare DNN performance on compute platforms for automated and assisted driving systems. Two years ago, AVCC set out to create recommendations for a benchmark to solve this issue," commented Kasper Ornstein Mecklenburg, Chair of AVCC's Micro-Benchmarks Working Group and Staff Performance Analysis Engineer at Arm. "This is the second of three technical reports on DNN benchmarking and it addresses the characteristics which make models and datasets suitable for this purpose, along with a list of recommended models and datasets."
Having a common view on how to benchmark machine learning (ML) in the automotive industry benefits the entire ecosystem.
For the automotive and ML communities, this will allow optimized use of models and datasets that are publicly available, and effectively address the key usage examples of DNNs in real-world automated and assisted driving systems applications.
"OEMs and automotive tier 1 suppliers can use the report to have a clear understanding of test results, allowing them to select the most suitable IP for their use-case. Plus, IP providers will better understand how to focus their research resources. It's a win-win for the industry," added Paul Hughes, AVCC Technical Chair and Lead System Architect/Distinguished Engineer at Arm.
The paper isn't limited to automotive audiences only: anyone who is involved in automation, including robotics and other ML applications, can benefit from the benchmarks outlined in this report.
For those interested in autonomous automotive and assisted driving, the TR-004 Models and Datasets technical report complements TR-003 Conditions and Reporting by adding which models, datasets, and automotive context are relevant for benchmarking.
To download the report, please visit the documents page on AVCC's website: https://www.avcconsortium.org/tr004/
For more information about AVCC and how it is serving component developers at automotive OEMs and its suppliers, including how to become a member, please visit http://www.avcconsortium.org.
About AVCC
AVCC is a global autonomous vehicle (AV) consortium dedicated to being the premier market enabler for intelligent software-defined vehicle technology. Membership touches upon every facet of the autonomous vehicle and software-defined vehicle design ecosystem, from technology suppliers to integrators and beyond. The Consortium serves systems and component developers at automotive OEMs and its suppliers with strategic programs and working group publications. AVCC is committed to driving the evolution from L1 to L5 performance over the next twenty years. At its core, AVCC is dedicated to providing a vetted architectural design and enabling a cooperative environment with algorithms and device interfaces for central and distributed compute for autonomous vehicles. http://www.avcconsortium.org
Media Contact
Armando Pereira, AVCC, 1 925.262.1024, [email protected]
Sarah LaLiberte, AVCC, 978.502.8558, [email protected]
SOURCE AVCC
Share this article