Where the constraint bites
The gap between algorithmic potential and deployable systems is where execution cost becomes a first order design variable. It holds at every scale.
- 01Energy
- Energy is the binding input now, on a battery and on a grid connection alike. What a workload costs per result decides how often anyone can afford to run it, and therefore what it is allowed to be used for.
- 02Latency
- Some decisions cannot wait for a round trip. A control loop closing in milliseconds has to resolve next to the data, which rules out anything that needs a datacentre in the path.
- 03Sovereignty
- Some data cannot lawfully leave the building, or the country. When the data cannot move, the computation has to come to it, and it has to fit whatever hardware is already on site.
- 04Access
- Capability that needs a hyperscaler budget is capability most people do not have. Efficiency is what decides whether a research group, a small team or a small country can build with this at all.
What we study
We study what happens when computational methods meet constrained systems. The work spans algorithm design and implementation, and the trade-offs that limited resources introduce across both.