NVIDIA Jetson Orin is the best-in-class embedded platform for AI workloads. One of the key components of the Orin platform is the second-generation Deep Learning Accelerator (DLA), the dedicated deep learning inference engine that offers one-third of the AI compute on the AGX Orin platforms. This post is a deep technical dive into how embedded developers working with Orin platforms can��
]]>NVIDIA Jetson Orin is the best-in-class embedded AI platform. The Jetson Orin SoC module has the NVIDIA Ampere architecture GPU at its core but there is a lot more compute on the SoC: The NVIDIA Orin SoC is powerful, with 275 peak AI TOPs, making it the best embedded and automotive AI platform. Did you know that almost 40% of these AI TOPs come from the two DLAs on NVIDIA Orin?
]]>A primary goal in the field of neuroscience is understanding how the brain controls movement. By improving pose estimation, neurobiologists can more precisely quantify natural movement and in turn, better understand the neural activity that drives it. This enhances scientists�� ability to characterize animal intelligence, social interaction, and health.
]]>Are you using the full potential of DLA on your NVIDIA Jetson module? Review this FAQ to make the most of your deep learning operations.
]]>On October 18, learn how to unlock one-third of AI compute on the NVIDIA Jetson AGX Orin by leveraging Deep Learning Accelerator for your embedded AI applications.
]]>If you��re an active Jetson developer, you know that one of the key benefits of NVIDIA Jetson is that it combines a CPU and GPU into a single module, giving you the expansive NVIDIA software stack in a small, low-power package that can be deployed at the edge. Jetson also features a variety of other processors, including hardware accelerated encoders and decoders, an image signal processor��
]]>Training AI models is an extremely time-consuming process. Without proper insight into a feasible alternative to time-consuming development and migration of model training to exploit the power of large, distributed clusters, training projects remain considerably long lasting. To address these issues, Samsung SDS developed the Brightics AI Accelerator. The Kubernetes-based��
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