NXP launched a deep learning toolkit called eIQ Auto. NXP is seeking to set itself apart from competitors by making its tool “automotive-quality.” NXP’s goal is to make it easier for AV designers to implement deep learning in vehicles.
NXP Semiconductors rolled out this week a new deep learning toolkit called eIQ Auto. NXP is seeking to set itself apart from competitors by making its tools “automotive-quality.” NXP’s goal is to make it easier for AV designers to implement deep learning in vehicles.
The development of autonomous vehicles (AV) does not necessarily require either artificial intelligence or deep learning. Simply put, not all AVs need to be AI-driven. And yet the rapid advancements and improved accuracy of deep learning are alluring to developers seeking to improve their highly automated vehicles.
AI is also expected to be applied to data fusion — vision with radar, for example. But again, the industry hasn’t reached consensus on when to fuse two species of sensory data. “Early fusion vs. late fusion is still being debated,” said Ors.
Today, most test AVs come with power-hungry hardware — which is not ideal for volume automotive production. NXP hopes its new eIQ toolkit enables customers to deploy powerful neural nets “in an embedded processor environment with the highest levels of safety and reliability.”
The AI framework and toolkit is also recommended by AI World Society (AIWS) to promote and apply openness and transparency in the constructive use and development of AI, including data sets, algorithms, intended impacts, goals, and purposes.
The original article can be found here.