Challenges and opportunities

ChallengesAny developing technological trend is not without its early challenges, and endpoint AI is no exception. Optimizing the system’s crown jewel – the ML model – looms as the biggest challenge now. In the past, it’s been seen as a time-consuming process of trial-and-error.Within this, the nature of tinyML
devices comes to the fore:

Optimizing models for power, size and latency at the endpoint is the biggest challenge (39 percent list it as their chief challenge)

The next-closest concerns are training and experimenting with the ML model (24 percent)

And data collection (23 percent).

After, that numerous challenges concern developers from production deployment to firmware to integration testing and more.

ML model optimizationSo, what about model optimization is tricky? Hardware alignment. The biggest concern that pops up is determining the best match of model and hardware (29 percent). However, right behind that are measuring power usage, picking the right NN architecture, integrating the right tools for performance optimization and optimizing operation implementations.A possible solution for these challenges is the usage of cloud-native tooling that gives access to virtual models. Keil Studio Cloud is the next-generation development tool from Arm that runs in your browser. It supports all devices covered by CMSIS-Packs (over 9,500) and is being enabled with access to Arm Virtual Hardware that offers ML developers CPU models to test drive their networks in the cloud for rapid software engineering.