Client work pays for the lab. The lab is what makes the client work worth buying.
AI ThinkLab keeps a standing internal programme rather than treating research as something that happens between projects. The rule we apply to it is narrow: we work on questions whose answers change what we would do on an engagement within about a year. Everything else is interesting reading, not a research line.
Current lines of enquiry
Low-bit quantisation past INT8
How far INT4 and mixed-precision schemes can be pushed on hardware whose compiler was designed for INT8 — where the accuracy actually goes, which layers refuse to cooperate, and when the dequantisation overhead quietly eats the gain.
Generative models at the edge
Small language and vision-language models on embedded SoCs: memory-bandwidth limits on decode, KV-cache strategies inside a fixed arena, speculative decoding when there is no second accelerator, and what a usable tokens-per-second actually feels like in a product.
Kernel and compiler autotuning
Search-based tuning of tiling, vector width and scheduling against real silicon rather than a cost model — and how much of the hand-written kernel work can honestly be automated before a human has to look at the disassembly again.
Evidence that an optimised model still behaves
Optimisation changes numerics. We are building the harness and the argument structure that lets a customer demonstrate to an auditor — or to themselves — that a quantised, pruned, re-scheduled model is still the model they validated.
How the programme is run
- Reproduce before believing. A published result counts once it runs on hardware we hold, at the numbers claimed.
- Measure on silicon, not in simulation. Cost models are a hypothesis; a board with a power meter on it is evidence.
- Publish the negative results. Knowing which technique does not survive an NPU compiler is worth as much as knowing which does.
- Feed it back into tooling. Anything learned twice becomes part of the harness, so the next engagement starts further along.
Publications & write-ups
Write-ups, reproductions and engineering notes are posted under Insights. Measured results go to Benchmarks. Where a piece of work is publishable in a venue rather than a blog post, it will be listed here.
Collaboration
We are open to joint work with university groups and with silicon vendors who want an independent evaluation of their toolchain — including one that says something they will not enjoy reading. Write to us if that describes you.
Sponsor a research line.
Some of what is on this page is directly useful to a company before it is useful to us. If one of them is on your critical path, it can be funded and accelerated.