KEYFACTS
Reducing costs and complexity
Time-to-market advantages
Application-ready modules and machine learning software optimize time-to-market and design security
Save development time and cost
Standardized hardware and software building blocks make application design more time and cost efficient
Engage new applications
Neural processing units bring powerful AI inference to new edge applications that was previously not possible due to power-hungry designs.
Scalability
Use only what you need and make full use of computing resources, optimizing energy consumption and cost
Cost savings
Established tool chains and open standards with a broad user base help to reduce NRE, improve design security, and increase ROI
Agility
The ability to easily upgrade without disrupting the entire system gives you agility to innovate

CONTENT
Why you should read this whitepaper
- Learn how SMARC Modules with i.MX95 SoCs enable powerful, low-power AI inference directly at the edge for real-time processing
- Facilitate the creation of robust AI solutions by deploying custom or pre-trained machine learning models on embedded systems.
- Experience how to design rugged applications in industrial temperature ranges that require minimal power and passive cooling.
- Get best practices on how to build compact and efficient AI-accelerated vision systems faster using standardized building blocks.
OUR AUTHOR
Meet The Author Who Made This Whitepaper Possible

Martin Danzer
Director Product Management

Director Product Management
Martin Danzer
Martin Danzer is Director Product Management at congatec. He studied electrical engineering at the Technical University of Deggendorf and has more than 20 years of experience in technical service, development management and product management for Computer-on-Modules, including his time at Kontron and JUMPtec AG.
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