Edge AI

Edge AI refers to the deployment of artificial intelligence applications on devices (“at the edge”) rather than in a centralized cloud environment.

Benefits

1. Privacy: Data (like video or audio) never leaves the device.
2. Latency: Real-time processing without network delays.
3. Bandwidth: No need to stream terabytes of raw sensor data to the cloud.
4. Reliability: Works without an internet connection.

Implementation

We implement Edge AI using:

• TinyML: Running optimized models on low-power microcontrollers (Cortex-M).
• FPGA Acceleration: Custom DPU (Deep Learning Processor Unit) overlays on AMD/Xilinx FPGAs for high-performance inference.

Related Terms

• FPGA — Reconfigurable hardware for custom AI accelerators.
• SoC — SoC FPGA platforms combining ARM cores with FPGA inference fabric.
• EU AI Act — The regulation governing AI systems, where edge processing simplifies compliance.

Explore our Edge AI & sensing solutions — from TinyML on Cortex-M to FPGA-accelerated DPU inference engines, designed for privacy-first, latency-critical industrial and defense applications.