Edge AI & TinyML
We move intelligence from the cloud to the device — training small models and running them on resource-constrained hardware.
Through TinyML workshops and edge-autonomy research, Giants learn to quantise and prune models, deploy to microcontrollers, and benchmark performance across chips — the foundation of offline, low-power intelligence.
Edge AI at nGOT
Countries in our community
Developers, undergraduates & graduates
Deep-tech R&D tracks in 2026
Projects shipped in a single sprint
People to impact within five years
Investment in every Giant
Ways We Work in Edge AI & TinyML
Hands-on TinyML workshops at KNUST: train small models, deploy to edge devices, compare chips.
Small Language Models for Edge Autonomy — quantising and pruning models to run locally.
Battery-free IoT combining indoor photovoltaics and RF energy harvesting with intermittent computing.
On-device perception, from lightweight vision to sensor fusion.
Giants Sessions on SLMs, energy harvesting, and edge autonomy.
Research sprints that turn ideas into working prototypes.
R&D track
Battery-Free IoT — Indoor PV & RF Harvesting
Combining energy harvesting with intermittent computing for long-lived sensors that never need a battery change.
Read more
