Uploaded January 2026 | Updated September 2026, 2 weeks ago
This video demonstrates how advanced AI/ML vision workloads can be deployed directly on resource-constrained edge devices without relying on the cloud. Using the Silicon Labs EFR32 MG26 SoC and ModelCat’s (formerly Eta Compute) Aptos platform, we showcase real-time face and gesture recognition running on an MCU with tight memory, power, and performance budgets.
The demo highlights how a dedicated machine learning hardware accelerator, combined with an efficient radio and low-power architecture, enables responsive vision inference at minimal energy cost. With concrete performance and power metrics, this solution illustrates how developers can bring intelligent, always-on interactions to everyday connected products like smart locks, switches, and lighting.
🔗 Learn more: silabs.com/applications/artificial-intelligence-machine-learning?source=Social&detail=YouTube&cid=soc-you-ml-011225
This video demonstrates how advanced AI/ML vision workloads can be deployed directly on resource-constrained edge devices without relying on the cloud. Using the Silicon Labs EFR32 MG26 SoC and ModelCat’s (formerly Eta Compute) Aptos platform, we showcase real-time face and gesture recognition running on an MCU with tight memory, power, and performance budgets.
The demo highlights how a dedicated machine learning hardware accelerator, combined with an efficient radio and low-power architecture, enables responsive vision inference at minimal energy cost. With concrete performance and power metrics, this solution illustrates how developers can bring intelligent, always-on interactions to everyday connected products like smart locks, switches, and lighting.
🔗 Learn more: silabs.com/applications/artificial-intelligence-machine-learning?source=Social&detail=YouTube&cid=soc-you-ml-011225
