As AI moves to the edge — from IoT sensors to embedded devices — energy efficiency becomes a hard constraint, not a preference. GreenSeal.dev brings research-grade energy profiling and optimization expertise to edge and embedded AI deployments, helping teams build systems that are both capable and energy-minimal.
What we offer
- TinyML & model compression — quantization, pruning, knowledge distillation, and architecture selection for on-device inference
- Hardware energy profiling — real measurements on NVIDIA Jetson Nano/Orin, Raspberry Pi 4/5, using Intel RAPL, NVIDIA SMI, and Monsoon Power Monitor
- End-to-end benchmarking — AI pipeline energy profiling from data ingestion to inference output, measured on target hardware
Grounded in peer-reviewed research
This capability is built on published research in energy-efficient deep learning, hardware energy profiling, and sustainable AI — work presented at ICSE 2025, CAIN 2023, and ICT4S 2025. Energy data is collected using EnergiBridge, the open-source cross-platform energy measurement toolkit co-developed by the GreenSeal founder with June Sallou and Thomas Durieux (TU Delft).
