Sponsored by: 
The proliferation of intelligent edge devices has fundamentally changed the interactions between humans and machines. Intelligent devices enrich the user experience, automate daily tasks, augment processes and workflows, contribute to business productivity and growth, and improve the overall wellbeing of human society.
One fundamental technology that contributes to these changes is edge Machine Learning (ML). By bringing inference workloads to the edge, ML implementations can be simplified and optimized for constrained environments in processing and power resources. Thanks to edge ML, intelligence is brought close to end-devices, allowing for faster and more reliable decision making while maintaining data security and privacy.
This whitepaper outlines key factors developers need to consider when choosing the best processing solution for their edge ML projects, namely tailored processing performance, improved energy efficiency, easy-to-use development environment, enhanced security, and extensive ecosystem. Focusing on these five factors will enable developers to facilitate their edge ML design, development, and deployment and capture the growth opportunities in the edge ML market.
Download the whitepaper to learn more.
