Discover how to overcome the hardware and software challenges of deploying vision-based AI at the edge – and accelerate the journey from prototype to production.
Demand for real-time, intelligent vision applications is growing across industries including manufacturing, automotive, transportation, retail, agriculture, security, aerospace and smart infrastructure.
But moving AI processing from the cloud to the edge brings a new set of development challenges. Software teams need to balance processing performance, latency, power consumption, memory and accuracy, while navigating different hardware architectures, software stacks and development frameworks.
This whitepaper explores the key considerations for developing vision-based AI at the edge and looks at how choosing the right hardware and development platform can help reduce complexity, risk and time to market.
Download the whitepaper to discover:
- The key stages involved in developing and optimising a vision-based edge AI application
- How GPUs, NPUs, CPUs, DSPs, FPGAs, MPUs, MCUs and TPUs compare
- The strengths and limitations of different software stacks, programming languages and frameworks
- How to balance inference performance, latency, power, memory and accuracy
- The benefits of vision-ready SOMs and pre-validated AI computing platforms
- How the right platform can reduce development time and simplify the route to production
The guide also examines how vision-ready SOMs can reduce hardware risk and development effort by providing pre-designed processing, memory and I/O alongside integrated camera interfaces and established software stacks.