Artificial Intelligence (AI) at Microchip
Integrating Intelligence Across Our Company
Artificial Intelligence (AI) is transforming how embedded systems are designed, developed and supported. From production-ready edge AI solutions to an AI coding assistant, our approach is to make innovative design easier for our customers.
The sections below highlight our engagement with AI across a variety of areas, including enabling others to build AI applications, offering AI tools and advancing our support for customers through a targeted acquisition and a long-term partnership.
Edge AI Solutions
We offer production-ready edge AI solutions that bring machine learning directly to embedded devices to process data locally, reduce latency, enhance privacy and enable real-time decision making without the need for cloud connectivity. Our portfolio spans applications across industrial, automotive and consumer domains, including arc fault detection, embedded vision, keyword spotting and predictive maintenance.
Our broad portfolio of products includes optimized silicon, embedded Machine Learning (ML) models and development tools that help teams deploy across microcontrollers (MCUs), microprocessors (MPUs) and Field-Programmable Gate Arrays (FPGAs) enabling a smoother transition from prototype to production.
MPLAB® AI Coding Assistant
AI at Microchip extends beyond silicon and into the tools engineers use every day. A key example is the MPLAB AI Coding Assistant, which brings AI-assisted capabilities directly into the development environment to help streamline design, coding and debugging.
Key capabilities include:
- Context-aware code assistance
- Faster code generation and refinement
- Intelligent debugging support
- Seamless integration with embedded workflows
By embedding AI within the development environment, the MPLAB AI Coding Assistant helps developers reduce complexity, accelerate development and maintain control of their designs.
Model Context Protocol (MCP) Server
As AI becomes more integrated into engineering workflows, access to accurate technical information is increasingly important. Our Model Context Protocol (MCP) Server allows AI systems to securely access structured product data and related documentation, helping improve context and response accuracy. Connecting AI models to trusted technical resources strengthens the reliability of AI-powered development environments.
This infrastructure supports:
- More relevant AI-driven technical answers
- Enhanced understanding of complex documentation
- Integration with AI-assisted tools
- Scalable access to technical knowledge
“The launch of our MCP Server is another example of how Microchip is leaning into AI and providing AI-based tools that help make life easier for our customers,” said Rich Simoncic, Chief Operating Officer for Microchip Technology.
Innovation and Strategic AI Expansion
We continue to expand our AI capabilities through targeted investments, portfolio expansion and innovation across multiple domains. One key strategic initiative is the acquisition of Neuronix AI Labs, which strengthened our embedded AI expertise and accelerated the development of on-device intelligence.
The investment enabled:
- Reduced power, size and calculations for tasks such as image classification, object detection and semantic segmentation while maintaining high accuracy
- Expanded support for power-efficient, AI-enabled edge solutions deployed on FPGAs
Another strategic initiative is our long-term partnership with Ceva to support AI acceleration. This collaboration enables scalable AI as a native capability across our compute, communication and security products. For more information refer to the related press release.
AI and Machine Learning Design Partners
AI innovation is strengthened through collaboration across hardware, software and system integration domains. We work with machine learning software providers, AI accelerator partners and authorized design partners to help support intelligent system development.
By combining our technology with a trusted partner ecosystem, our customers move from concept to deployment more efficiently.
