Edge AI Embedded Systems
Architecture-driven integration of AI workloads into embedded platforms, delivering deterministic performance, security, and scalable deployment.
Key Capabilities
01AI model deployment on embedded SoCs
02Hardware acceleration (NPU / GPU / DSP) integration
03Real-time inference optimization
04Memory and power footprint tuning
05Secure AI runtime integration
06Edge-to-cloud data orchestration
07Safety-gated inference architecture — validating AI decisions against safety constraints before actuation (the DECS design center)
08Closed-loop, control-theoretic edge control (real-time sense–infer–act loops)
09From-scratch NPU enablement: Vela / Arm Ethos-U bring-up demonstrated from scratch at 0.35–0.46 ms per inference, plus eIQ Neutron, Neural-ART, C7x-MMA, and train-on-NVIDIA-GPU / deploy-to-Jetson-with-TensorRT targets
Architect Your Next Mission-Critical Platform
For safety-critical, high-performance, and security-sensitive systems
Engage Edges AI Systems to architect, secure, and deliver deterministic embedded and Edge AI platforms — from silicon bring-up to real-world deployment.