AI vision implemented on edge MCUs using Nvidia TAO demonstrated
Alif Semiconductor® and Edge Impulse have announced a significant breakthrough in implementing AI vision processing on edge microcontrollers (MCUs), through the introduction of full support for the Nvidia TAO model training toolkit on the Ensemble® and Balletto™ MCU families and the Edge Impulse platform.
The TAO toolkit delivers extensive training datasets for common vision processing applications, and can use transfer learning from pre-trained models. This innovation promises to substantially reduce the cost, time and effort which embedded device OEMs typically invest into model development for AI vision applications on edge MCUs.
Until now, deploying TAO-trained models on low-power MCUs for edge applications has been untested and unproven. Now, with the complete integration of Nvidia TAO into its platform, Edge Impulse has established a streamlined process for deploying TAO models on the Alif Ensemble and Balletto families of MCUs and fusion processors. Fully integrated into the Edge Impulse platform, Alif’s products feature the Arm® Ethos™-U55, a neural processing unit (NPU) for which Nvidia has optimized the TAO toolkit.
Embedded developers seeking to implement AI vision applications, such as people counting, intruder detection, or robotics, can now utilize the TAO training toolkit and its dataset, confidently deploying either a pre-trained TAO model or a custom model developed through transfer learning via Edge Impulse on Alif Ensemble or Balletto MCUs.
Henrik Flodell, Senior Marketing Director at Alif Semiconductor, stated, “With Alif’s AI-optimized MCU ecosystem, high-end embedded vision processing is transitioning from large, expensive microprocessors to next-generation edge MCUs. The integration of the TAO toolkit by Edge Impulse has significantly streamlined the development and deployment of AI vision processing models on Alif MCUs.”
Adam Benzion, SVP Partnerships at Edge Impulse, added, “The TAO toolkit accelerates the generation of effective ML models, but it doesn’t address the deployment of these models on edge-optimized hardware. Together with Alif, we have solved this challenge by providing a fully integrated workflow from the TAO toolkit’s pre-trained models to deployment on Alif edge MCUs.”
Visitors to Electronica can view the Alif/Edge Impulse demonstration of advanced AI vision processing on an Ensemble MCU created using transfer learning supported by the TAO toolkit.
https://alifsemi.com
www.edgeimpulse.com
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