Automotive Task Force delivers POC in first milestone towards a 1.0 Automotive Benchmark Suite
David Kanter
Today our MLCommons MLPerf Automotive benchmark taskforce has achieved a major milestone towards building a full automotive benchmark suite, with the release of the automotive benchmark proof-of-concept.
As we all know, demand for AI-based systems in vehicles is exploding – and not just for controlling fully autonomous cars. Automotive OEMs are incorporating AI in a vast array of in-car systems to enhance the driving experience and increase vehicle safety, for example:
Speech-controlled infotainment systems and online vehicle user manuals
Route/direction guidance systems
Optimizing stops for charging, refueling, and maintenance
Collision avoidance systems
Driver monitoring for drowsiness or lack of attention
All of these features require trained AI models and appropriate input sensors, plus underlying computing infrastructure powerful enough to meet performance demands, and OEMs need common reference points to understand the collective computing demand of the systems and the resources required to meet it. The MLPerf Automotive benchmark will provide those common reference points to enable OEMs the insight needed to select or design the most suitable solution for their system requirements with provisioned resources.
Thank you to our Automotive benchmark task force which includes representatives from Arm, Bosch, Cognata, cKnowledge, Marvell, NVIDIA, Qualcomm Technologies, Inc., Red Hat, Sacramento State University, Samsung, Siemens EDA, Tenstorrent, and UC Davis amongst others.
This is important work and we encourage others, in particular those with connections to automotive AI, to join the task force to ensure we are comprehensive in building a v1.0 product later this year.
Learn more in todays blog at https://mlcommons.org/2024/06/automotive-benchmark-poc/
Thanks!