AAEON Europe has officially released support on the latest Yocto 3.1 Dunfell on AAEON AI embedded box PC, BOXER-8220AI, BOXER-8221AI...
AAEON is honored with receiving the 29th Taiwan Excellence Award for 2021. Four AAEON products are recognized this year ...
The new AAEON products provide key interfaces needed for edge computing in a small form factor, making it easier to build applications for all levels of users, from makers to more advanced developers for deployments in the field. The new products include:
- BOXER-8251AI featuring the new Jetson Xavier NX, a powerful, compact AI processing module with 384 CUDA® cores, 48 Tensor Cores, and two NVDLA engines capable of running multiple neural networks in parallel and delivering processing speeds up to 21 TOPS
- BOXER-8240AI featuring the Jetson AGX Xavier, which delivers workstation performance in a compact form factor with 512 CUDA cores and 64 Tensor Cores delivering processing speeds up to 32 TOPS
- BOXER-8222AI featuring the Jetson Nano, capable of operating multiple neural networks or processing several high-resolution images at the same time with 128 NVIDIA CUDA cores delivering processing speeds up to 472 GFLOPS
AAEON also introduced a new version of the popular BOXER-8120AI, now featuring the Jetson TX2 4GB module, providing an efficient and cost-effective solution for AI edge computing with 256 CUDA cores delivering processing speeds up to 1.3 TFLOPS.
“Partnering with an AI and edge computing leader like NVIDIA supports our mission to deliver more diversified embedded products and solutions at higher quality standards,” said Alex Hsueh, Senior Director of AAEON’s System Platform Division. “These new offerings powered by the Jetson platform complement our existing lineup of rugged embedded products, providing an optimal combination of performance and price in a smaller form factor for customers to easily deploy across a full range of applications.”
AAEON is a partner in the NVIDIA Jetson ecosystem. The NVIDIA Jetson family of products provides the performance and power efficiency to run autonomous machines faster and with less power in a compact form factor, bringing the power of modern AI, deep learning, and inference to embedded systems at the edge.
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