The announcement comes at a time when the combination of AI and IoT, known as the Artificial Intelligence of Things (AIoT), is providing machine learning capabilities in connected devices, enabling them to perform intelligent tasks. The AIoT market is forecast to increase significantly in the coming years with some estimates valuing it over $16 billion by 2024.
Infineon's ModusToolbox ML is a new feature in ModusToolbox Software and Tools that provides middleware, software libraries and special tools for designers to evaluate and deploy deep learning-based ML models. This feature allows seamless integration with existing frameworks available in ModusToolbox so that ML workloads can be integrated into secured AIoT systems. The toolset provides a streamlined machine learning model deployment workflow that will allow developers to be more efficient and get to market faster.
ModusToolbox ML allows developers to use their preferred deep learning framework, such as TensorFlow, to be deployed directly to PSoC MCUs. In addition, the feature helps designers optimise the model for embedded platforms to reduce size and complexity, as well as validate performance against test data.
“As the IoT scales, massive amounts of data are being generated at the edge. Enabled by TinyML, AIoT is a natural evolution, where acting on data locally helps manage data privacy, latency and overall system reliability,” said Steve Tateosian, Vice President of IoT Compute and Wireless at Infineon. “ModusToolbox bridges a critical gap between machine learning and embedded systems design by providing flexible tools and modular libraries to easily optimize, validate and deploy deep learning models from popular training frameworks on Infineon’s ultra-low power microcontrollers.”
