At the Hardware Pioneers Max 2023 event, a significant breakthrough in machine learning was unveiled by Edge Impulse, in collaboration with Alif Semiconductor. The partnership has led to the development of innovative machine learning solutions that operate on the edge, transforming the way we approach AI and IoT applications.
The demo that we were shown simplifies the process of running machine learning models on the edge. This platform leverages Alif Semiconductor's latest Arm Cortex M55 and Arm Ethos U55, enabling machine learning to be run on a microcontroller for low power applications.
The Edge Impulse platform offers a comprehensive solution for edge AI applications. It allows for data collection, model training, testing, and deployment, all on the edge. This eliminates the need for constant cloud connectivity, offering a significant speed advantage and enhanced privacy. The platform supports TensorFlow Lite as a compilation, allowing the Ethos U55 accelerator to handle the machine learning tasks while the CPU continues to perform other functions.
The applications of this technology are vast and transformative. In the medical field, where privacy is paramount, this technology allows sensitive data to remain on the edge, enhancing patient privacy. Similarly, in wearable applications, all processing can be done on the edge, ensuring full privacy and compliance with security standards.
Face Detection On The Edge
One of the most impressive demonstrations of this technology was a face detection model running live on the edge. This model, trained in the cloud, was running on Alif Semiconductor's latest development kit, showcasing the power and potential of machine learning on a microcontroller. This technology could revolutionize access control systems, enabling face recognition for family members and eliminating the need for keys.
Edge Impulse and Alif Semiconductor's partnership is a testament to the power of collaboration in driving innovation. By combining their expertise, they have created a solution that is not only powerful and efficient but also respects user privacy. This technology is set to revolutionize the way we approach machine learning on the edge, opening up new possibilities for AI and IoT applications.