Edge AI: running ML models on microcontrollers and embedded devices

Short summary

Edge AI runs ML models on microcontrollers and embedded devices with kilobytes of RAM, addressing cloud AI's limitations in latency, privacy, connectivity, and cost. The hardware spectrum ranges from ARM Cortex-M and ESP32 to Raspberry Pi and NVIDIA Jetson. Firmware engineers play a critical role in deploying models within tight memory and power budgets across applications like smart cameras, predictive maintenance, medical devices, and wearables.

• •Edge AI brings inference to microcontrollers, solving cloud AI's latency, privacy, and connectivity gaps
• •Hardware spectrum spans ARM Cortex-M (low-power) to NVIDIA Jetson (GPU-accelerated vision)
• •Deployment is a firmware engineering challenge: real-time, memory-constrained, power-budgeted

Generated with AI, which can make mistakes.