Infineon advances Edge AI computing on PSOC™ Edge with NVIDIA® TAO toolkit integration
Infineon Technologies AG announces support for NVIDIA TAO models on the PSOC™ Edge MCU family, enabling developers to accelerate AI model deployment in low-power MCUs.
www.infineon.com
Infineon's PSOC Edge MCU family features Arm® Cortex®-M55 processors and Ethos-U55 microNPU and enhances energy-efficient machine learning capabilities at the edge. The combination of these versatile microcontrollers with the powerful NVIDIA TAO models and fine-tuning toolkit greatly simplifies the creation, customization, optimization, and deployment of high accuracy vision AI models.
“NVIDIA is a key player in the AI/ML space, so we and our lead customers engaged in vision, audio and voice applications are very excited about this effort,” said Steve Tateosian, Senior Vice President IoT Compute & Wireless at Infineon. “By integrating NVIDIA TAO in our PSOC Edge portfolio, we empower developers to create smarter and more efficient systems that work at the edge of the network and solve real-world challenges with speed and precision. This significantly speeds up time-to-market for ML enabled applications in industrial automation, medical, automotive, and smart IoT solutions.”
“NVIDIA TAO brings the latest advances in computer vision models and fine-tuning workflows to the far edge”, said Deepu Talla, Vice President of Robotics and Edge Computing at NVIDIA. “Infineon PSOC Edge’s integration of NVIDIA TAO greatly simplifies the development and deployment of customized AI across a range of devices.”
The integration of NVIDIA TAO features will become available for the entire PSOC Edge family along with a comprehensive development ecosystem, including tools, libraries, and documentation, to accelerate innovation and reduce time-to-market for AI-enabled applications.
Infineon delivers full system solutions for Edge AI. Bringing intelligence to the edge allows for new and advanced IoT use-cases as it improves system determinism, privacy and security while also reducing latency, system power as well as cost for AI-enabled solutions.
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