AI-powered tool streamlines embedded software development

Microchip has launched the MPLAB® AI Coding Assistant, which provides code writing and debugging tools, and a chatbot for personalized support and advice for developers of embedded systems running on Microchip products. 

Microchip has harnessed the capability of AI to provide a new interactive, real-time support tool which helps developers to create better software more quickly and more easily. 

The new MPLAB AI Coding Assistant helps software developers and embedded engineers to write and debug code. A Microsoft® Visual Studio® Code extension, the free tool is based on Continue, the market’s leading open-source AI code assistant, and is supplied with a Microchip AI chatbot for real-time support.

The chatbot enables developers to evaluate and iterate code directly from the sidebar. This interactive support enhances the coding experience by providing highly customized, relevant real-time assistance and insights into Microchip-specific products.

Additional features include advanced auto-complete for easier coding, an edit feature, and error detection for efficient code modifications within the current file. The tool also provides access to searchable Microchip documentation within the MPLAB integrated development environment.

Unlike most other code assistants on the market, the MPLAB AI Coding Assistant sidebar chat feature can produce block diagrams directly within the VS Code interface, rather than text responses only. This capability, combined with easy access to a library of documentation on Microchip microcontrollers and microprocessors, streamlines the coding process and helps to enhance accuracy.

Other AI-based advances from Microchip include the release of the PolarFire® FPGA Ethernet Sensor Bridge, a solution which enables the development of AI-driven sensor processing systems compatible with the NVIDIA Holoscan platform1. 

Microchip has also partnered with Cartesiam, Edge Impulse, and Motion Gestures to simplify development in MPLAB X of machine learning implementations at the edge running on Microchip Arm® Cortex®-based microcontrollers and microprocessors.

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