Custom Meta Tags

ceros-hero-banner

Main Title

Deploy Edge AI on Embedded Systems

intro-html-K

Production-ready solutions for real-world applications.

AI is transforming how engineers design embedded systems, enabling real-time decision making, reduced latency, enhanced privacy, and operation without constant cloud connectivity. Microchip's Edge AI portfolio combines MCUs, MPUs, FPGAs, development tools, reference designs, and proven application solutions that help accelerate deployment from proof of concept to production. Whether you're building intelligent industrial equipment, smarter infrastructure, secure access systems, or voice-enabled devices, Microchip provides the technologies and resources to help bring Edge AI to market faster.

body-title-featured edge ai -K

Featured Edge AI Resources

body-mm-predictive-maint-Kk

PREDICTIVE MAINTENANCE WITH EDGE AI

Move beyond reactive maintenance with embedded machine learning for real-time machine health monitoring. Analyze sensor data at the edge to identify anomalies earlier, minimize downtime, and improve operational reliability.

View Predictive Maintenance Case Study

Explore a real-world implementation of machine health monitoring.

Production-Ready Edge AI for Machine Health Monitoring

Production-Ready Edge AI for Machine Health Monitoring. Learn how embedded AI transforms sensor data into actionable maintenance insights.

body-mm-voice-activated-maint-K

VOICE ACTIVATED AT THE EDGE WITH MICROCHIP AND /IOTCONNECT

See how always-on voice interfaces can run locally on embedded systems using Keyword Spotting. Extend those solutions with /IOTCONNECT for deployment, monitoring, and lifecycle management of connected Edge AI devices.

ACCESS THE KEYWORD SPOTTING DEMO

Voice Activation at the Edge with AWS, /IOTCONNECT, and Microchip

Keyword Spotting Solution | Microchip Technology

Design and develop embedded Linux, industrial, and HMI applications with the Microchip EV63J76A SAMA7D65 Curiosity Kit.

  • Edge processing on the Microchip SAMA7D65 platform

Responsive Image-logo-K

rr-build edge ai - tools - K

Build Edge AI on the Right AI Tools

From low-power MCUs to high-performance MPUs and FPGA-based acceleration, Microchip offers scalable hardware platforms for deploying Edge AI applications from prototype to production.

Explore Solutions

rr-deploy-edge-ai-linux-K

Deploy Edge AI on Embedded Linux Systems

Evaluate and prototype Edge AI applications using the Microchip SAMA7D65 Curiosity Kit. Develop embedded Linux, HMI, industrial, and intelligent edge solutions on a production-ready MPU platform.

Explore Kit

Contact Sales Modal

rr-contact-us-K

Talk with an Edge AI Expert

Need help selecting the right hardware, tools, or development path? Connect with an Avnet specialist to discuss your Edge AI application and accelerate your design.

Contact us 

 

rr-edge-ai-for-medical-K

Edge AI for Medical Devices

Learn how embedded AI and machine learning are being applied in diagnostic imaging, patient monitoring, wearables and other connected medical devices. Explore the benefits of local data processing for applications where latency, privacy and reliability are critical.

Read blog 

body-html-production-ready-K

Production-Ready Edge AI Solutions

These production-ready edge AI solutions are designed to move beyond prototypes and into real-world deployment. Each solution combines optimized silicon, embedded machine learning models and development tools to help teams bring intelligent systems to market faster and with greater confidence.

Facial Recognition with Liveness Detection

Secure Identity Verification at the Edge
Use embedded AI to perform facial recognition and liveness detection directly on the device. On-device processing helps improve security, reduce cloud dependence, and protect sensitive data while delivering fast, reliable identity verification in real-world environments.

Arc Fault Detection

Protect Critical Systems with Edge AI
Use embedded machine learning to detect arc faults in real time and respond faster to potential electrical hazards. By performing analysis directly on the device, engineers can improve reliability, increase uptime, and enhance safety without relying on cloud connectivity.