Microchip Technology has partnered with Cartesiam, Edge Impulse and Motion Gestures to simplify ML implementation at the edge using the company’s ARM Cortex based 32-bit micro-controllers and microprocessors in its MPLAB X Integrated Development Environment (IDE). Bringing the interface to these partners’ software and solutions into its design environment uniquely positions Microchip to support customers through all phases of their AI/ML projects including data gathering, training the models and inference implementation.
“Adoption of our 32-bit MCUs in AI-at-the-edge applications is growing rapidly and now these designs are easy for any embedded system developer to implement,” said Fanie Duvenhage, vice president of Microchip’s human machine interface and touch function group. “It is also easy to test these solutions using our ML evaluation kits such as the EV18H79A or EV45Y33A.”
Cartesiam, founded in 2016, is a software publisher specialising in artificial intelligence development tools for microcontrollers. NanoEdge AI Studio, Cartesiam’s patented development environment, allows embedded developers, without any prior knowledge of AI, to rapidly develop specialised machine learning libraries for microcontrollers. Devices leveraging Cartesiam’s technology are already in production at hundreds of sites throughout the World.
Edge Impulse is the end-to-end developer platform for embedded machine learning, enabling enterprises in industrial, enterprise and wearable markets. The platform is free for developers, providing dataset collection, DSP and ML algorithms, testing and highly efficient inference code generation across a wide range of sensor, audio and vision applications. Get started in just minutes thanks to integrated Microchip MPLAB X and evaluation kit support.
Motion Gestures, founded in 2017, provides powerful embedded AI-based gesture recognition software for different sensors, including touch, motion (i.e. IMU) and vision.
Unlike conventional solutions, the company’s platform does not require any training data collection or programming and uses advanced machine learning algorithms.
As a result, gesture software development time and costs are reduced by 10x while gesture recognition accuracy is increased to nearly 100 percent.