Microchip Technology, microcontroller, mixed-signal, analog and Flash-IP integrated circuits manufacturer, has partnered with Cartesiam, Edge Impulse and Motion Gestures to simplify machine learning (ML) implementation at the edge using it’s ARM Cortex based 32-bit micro-controllers and microprocessors in its MPLAB X Integrated Development Environment.
September 16, 2020. By Manu Tayal
Microchip Technology, a manufacturer of microcontroller, mixed-signal, analog and Flash-IP integrated circuits, has partnered with Cartesiam, Edge Impulse and Motion Gestures to simplify machine learning (ML) implementation at the edge using it’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.
Commenting on the development, Fanie Duvenhage, vice president of Microchip’s human machine interface and touch function group, said “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.”
“It is also easy to test these solutions using our ML evaluation kits such as the EV18H79A or EV45Y33A,” Duvenhage added.
Cartesiam is a software publisher specializing in artificial intelligence (AI) development tools for microcontrollers. NanoEdge AI™ Studio, Cartesiam’s patented development environment, allows embedded developers, without any prior knowledge of AI, to rapidly develop specialized machine learning libraries for microcontrollers.
Edge Impulse is the end-to-end developer platform for embedded machine learning, enabling enterprises in industrial, enterprise and wearable markets.
Motion Gestures 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.
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