Infineon Technologies has unveiled ModusToolbox Machine Learning (ML). It enables deep learning-based workloads on Infineon’s PSoC microcontrollers (MCUs).
“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.”
According to Markets and Markets, the AIoT market is expected to increase from US$5.1 billion in 2019 to US$16.2 billion by 2024, growing at a CAGR of 26 percent.
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 easily integrated into secured AIoT systems. The rich toolset provides a streamlined machine learning model deployment workflow that allows developers to be more efficient and deliver quality products 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 optimize the model for embedded platforms to reduce size and complexity, as well as validate performance against test data.
ModusToolbox ML delivers an unmatched developer experience that reduces the complexities system developers face when developing AIoT applications. These applications typically require a seamless Machine Learning workload integration, along with computing, connectivity and cloud domains that ModusToolbox ML can address.