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Scalable Inferencing for Autonomous Trucking

June 23, 2022

Scalable Inferencing for Autonomous Trucking

Most AI inferencing requirements are outside the datacenter at the edge where data is being sourced and inferencing queries are being generated. AI inferencing effectiveness is measured by the speed and accuracy with which answers are provided, and, in many applications, real-time response is required.  To meet the objectives of many AI applications, a very large number of inferencing queries need to be serviced simultaneously.  Often many different inferencing models answering different types of queries need to be coordinating in parallel.

Autonomous trucking is a prime example. To achieve AI Level 4 (no driver) in autonomous trucks, powerful AI inference hardware supporting many different inferencing engines operating and coordinating simultaneously is required.  A long-haul truck in Boston with cargo destined for the west coast will spend two days autonomously operating across the country, saving its owner cost and time.  When driving all day and night, it will encounter a range of variable weather and traffic conditions, as well as some unexpected events like animal crossings, accidents, construction detours or debris in the road.

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