How to train automotive AI faster

The key battleground for automotive stakeholders over the next three years will be in achieving the production of electric, connected and autonomous vehicles at scale. Today, significant progress is being made in improving the real-time inference accuracy of a car…

How to train automotive AI faster

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The key battleground for automotive stakeholders over the next three years will be in achieving the production of electric, connected and autonomous vehicles at scale. Today, significant progress is being made in improving the real-time inference accuracy of a car through advanced sensor technologies such as LiDAR.

However, rapidly developing complex neural networks in a GPU-powered datacenter before deploying them in a car remains a significant challenge for automotive companies.

In this webinar, Keith Rieken, Solutions Manager, Artificial Intelligence at Pure Storage, discusses the insight that Pure has gained by working closely with the pioneers of real-world AI. Rieken also presents a new benchmark suite for evaluating and tuning input pipelines, as well as diagnostic techniques to improve the accuracy of a neural network model.

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