NVIDIA and Open Robotics have entered into an agreement to accelerate ROS performance on NVIDIA’s Jetson edge AI platform and GPU-based systems. These initiatives are designed to reduce development time and improve performance for developers seeking to incorporate computer vision and AI/machine learning functionality into their ROS-based applications. 

AI models have become more complicated in recent times due to the escalating demand for real-time AI applications in various industries. This necessitates the deployment of high-performing, cutting-edge inference systems in an optimal way. TensorRT's latest version addresses these issues by bringing in additional capabilities to provide more enhanced and responsive conversational AI applications to their customers. 

NVIDIA maintained its position in the global market for artificial intelligence (AI) processors used in the cloud and in data centers in 2020, with an 80.6% share of global revenue, according to Omdia.

In this week’s Embedded Insiders, Brandon and Rich try to decide if data sheets specs are reliable, or if industry benchmarks are the only reasonably- accurate measure of component performance without actually testing them yourself.

How can you design high-performance systems without a high-performance development platform?

Used for intelligent video analysis, computer vision, robotics, and autonomous driving, Nvidia also added an industrial variant of the Jetson AGX Xavier for harsh environmental conditions.

Here’s a fact that won’t shock you—the automotive industry is in the midst of a huge transformation thanks to AI technology. Here’s another fact that won’t be a surprise—NVIDIA is smack in the middle of that transformation.