AI IN AV
AUTONOMOUS DRIVING IS ON THE VERGE OF TRANSFORMING THE TRANSPORTATION INDUSTRY.
Applications of AI in AV
AI-based algorithms are present in all levels of automation.
Why is gaudi a good fit for AV Use Cases?
To adapt to ever-changing road conditions, the autonomous driving industry needs scalable compute infrastructures that can handle many large distributed ML training jobs. There is an increasing need for frequent retraining and updating of models to reach generalizability across different location conditions.
The two primary considerations that come into play when employing AI processing are time- and cost-to-train. Habana’s Gaudi Training Processors are expressly designed—in both hardware and software—to deliver high-efficiency cost- and time-to-train, making AI training more accessible to more organizations and for more applications.
First-generation Gaudi delivers up to 40% better price-performance than comparable GPU-based solutions—for both the EC2 DL2 instance and on-premise systems. Training with Gaudi clusters is available both in the cloud with AWS EC2 DL1 instances consisting of 8 Gaudis and on-premises with the Supermicro X12 Gaudi Training Server also consisting of 8 Gaudis. In addition, Gaudi2, which launched in May 2022, offers substantial performance advances that enable significantly faster training of models while preserving cost-efficiency. Gaudi2 systems will be available from Supermicro in 2H 2022 for on-premises implementation.
News and customer testimonials
“As a world leader in automotive and driving assistance systems, training cutting edge Deep Learning models is mission-critical to Mobileye business and vision. As training such models is time consuming and costly, multiple teams across Mobileye have chosen to use Gaudi-accelerated training machines, either on Amazon EC2 DL1 instances or on-prem; Those teams constantly see significant cost-savings relative to existing GPU-based instances across model types, enabling them to achieve much better Time-To-Market for existing models or training much larger and complex models aimed at exploiting the advantages of the Gaudi architecture. We’re excited to see Gaudi2’s leap in performance, as our industry depends on the ability to push the boundaries with large-scale high performance deep learning training accelerators.”
Executive Vice President of R&D
“On our own models the increase in price performance met and even exceeded the published 40% mark.”
“We are consistently seeing cost-savings compared to existing GPU-based instances across model types, enabling us to achieve much better time-to-market for existing models or training much larger and complex models.”
DevOps Tech Lead & Specialist
 The Business Research Company, Autonomous Cars Global Market Report 2022, available at https://www.thebusinessresearchcompany.com/report/autonomous-cars-global-market-report