Energy-efficient edge AI deployment
A model that needs a server rack per machine is not a product. We size the model for the hardware and the site's power budget, and we measure it on the board.
A notebook is not a sealed unit bolted to a gearbox
The gap between a model that works in a notebook and a model that runs on a sealed unit bolted to a gearbox is wider than most projects budget for. Memory that was free on a workstation is the binding constraint. A preprocessing library that allocates inside a loop can, for example, turn a 4 ms inference into a 40 ms one. A thermal limit at, say, 45 °C throttles the board halfway through a summer shift.
We treat the target device as part of the design rather than an afterthought. Hardware class is chosen against the latency and duty cycle the process actually needs, the model is compressed and quantised against measured accuracy loss rather than a rule of thumb, and everything is profiled on the real board under load.
Then there is the part nobody photographs: keeping a fleet of devices in the field honest. Versioned models, staged rollout, a rollback path, and monitoring of the input distribution as well as the output, so drift is visible before accuracy falls.
What is included
Hardware selection (microcontroller, embedded GPU, NPU); model compression and quantisation; latency and memory profiling; test harness and hardware-in-the-loop validation; monitoring and drift strategy; MLOps for fleets of edge devices.
Hardware selection with evidence
Microcontroller, embedded GPU or NPU chosen against measured latency, memory and power on your workload, not on a datasheet.
A compressed model with a measured accuracy budget
Quantisation and pruning taken exactly as far as the accuracy target allows, and no further.
Hardware-in-the-loop validation
Recorded plant data replayed through the deployed model on the actual device, outputs compared against the reference implementation, power logged.
Fleet MLOps
Model versioning, staged rollout, rollback, and input-and-output monitoring across the deployed fleet.
Four stages, each with an output you can check
Profile the workload
Sample rate, window length, duty cycle, latency budget and the power envelope the site can actually provide.
Compress and quantise
Against a written accuracy budget, with the trade-off curve shown rather than a single chosen point.
Hardware-in-the-loop
On the target board, under thermal and load conditions that match the installation.
Fleet roll-out
Provisioning, staged deployment, monitoring and the rollback path, proven before the fleet grows.
Sectors that buy this most often
What clients ask before they start
How small can these models actually get?
Small enough for a microcontroller, for many acoustic and vibration tasks, because the useful information sits in a time-frequency representation that is cheap to compute. The honest answer for your case comes out of a profiling exercise, not a brochure.
Can you work with hardware we have already chosen?
Yes, and we will tell you plainly if it cannot meet the latency or memory budget, before you buy a fleet of them.
What does 'energy-efficient' mean in numbers?
Measured milliwatts and milliseconds on your board, logged during hardware-in-the-loop testing, reported alongside accuracy. We do not quote efficiency figures we have not measured on the target.
Ask about Energy-efficient edge deployment
Tell us the asset or process and what goes wrong. We answer every enquiry within 48 hours on working days, and the first call is free.
- NDAs signed before the first call if you prefer
- Anonymised or synthetic samples are fine to start
- Your data is never used to train models for anyone else
Services clients combine with this one
Ready to find out if it works on your machines?
Start with a free 30 to 45 minute discovery call. Bring the problem, the recordings you already have and your questions. We will tell you honestly whether sensing-based AI is the right tool and what the next step would cost in days, not months.