Edge AI that runs where the data is.
Most AI still requires a data centre, a cloud subscription, and a continuous data feed. It burns energy moving bytes rather than acting on them. Edge AI takes a different view: intelligence belongs at the source, running on the hardware already in the field. The question is which algorithm gets you there. ModelMill trains models purpose-built for the edge — small enough to fit on a microcontroller, fast enough for real-time control, and accurate enough to matter.
Scalable edge AI for any embedded application
From a microcontroller inside a hearing aid to the processor managing an industrial robot, edge AI target devices span an enormous range of hardware. What they share is the need for intelligence that fits — on the chip, within the power budget, and inside the real-time constraint. ModelMill trains for all of them.
Automotive
In-cabin sensing, ADAS feature extraction, and predictive health monitoring — running directly on the ECU.
Drones & defence
Autonomous classification and anomaly detection in air-gapped, battery-constrained platforms.
Robotics
Real-time fault detection and state classification on the robot's own embedded controller.
Industrial automation
Vibration, current, and acoustic monitoring running on the PLC or sensor MCU — no cloud round-trip.
Internet of Things
Intelligence at the node, not the gateway — years of battery life with inference running continuously.
Predictive maintenance
Bearing faults, pump cavitation, motor degradation — detected on the asset itself, before failure.
Wearables & medical
Body-worn sensing, edge AI hearing aids, and health monitoring where data must not leave the device.
Intelligence that fits the hardware you already have.
The edge is not a scaled-down data centre. It is a completely different set of constraints — and the best edge AI is not a neural network that has been compressed into submission. It is a model architecture designed from the ground up to work under them. Logic-Based Networks, trained through ModelMill, are that architecture.
Runs on MCUs and CPUs
From low-power microcontrollers to industrial processors. No floating-point unit required. No GPU. No NPU. No Nvidia Jetson. The model runs on the 32-bit chip already inside your device.
Battery or mains powered
Efficient enough for years on a single cell. Fast enough for real-time control. At 52× less energy per inference than neural network equivalents, battery life is measured in months rather than hours.
Minimal memory footprint
Small models. Predictable performance. Deterministic behaviour. The MLPerf Tiny anomaly detection model is 7.29 KiB. It fits on every industrial MCU made.
Explainable and deterministic by design
Every decision can be inspected. No opaque inference paths. The logical rules driving each classification can be read and audited — making these models deployable in regulated, safety-critical, and certification-constrained applications.
No connectivity required
Inference runs entirely on-device. The data is processed where it is generated, the decision is made immediately, and nothing is transmitted. Edge AI without the cloud dependency is not a feature — it is the point.
Tiny edge. Larger edge. One training platform.
Edge AI covers a wide spectrum of hardware. At one end: microcontrollers with kilobytes of SRAM, no operating system, and a power budget measured in microwatts. At the other: industrial single-board computers and embedded systems with gigabytes of RAM and multi-core processors. The constraint changes, but the principle does not — intelligence should run on the device, not depart from it.
ModelMill trains for both. Its AutoML approach searches the model architecture space automatically, targeting the specific hardware profile you specify. The result is a model sized and optimised for your device — not a generic export that may or may not fit.
Tiny edge: microcontrollers
Arm Cortex-M0, Cortex-M4, ESP32, and similar. SRAM in the tens of kilobytes. No operating system. Always-on, battery-powered, embedded inside the asset. These are the devices where neural networks cannot run — and where Logic-Based Networks do.
Embedded systems
Raspberry Pi, industrial SBCs, and mid-range embedded Linux platforms. More capable, but still power- and footprint-constrained compared to cloud hardware. Ideal for edge AI cameras, edge AI hearing aids, audio processing, and multi-sensor fusion tasks requiring a richer runtime environment.
Edge servers and gateways
Higher-compute edge nodes aggregating data from multiple sensors. These handle more complex inference tasks, but the same principle applies: on-site processing, no upstream data transfer, real-time decisions. Even here, a smaller, faster model is a better model.
Neural networks scale up
LBNs scale down.
Neural networks assume abundant compute. They were designed for data centres with GPU racks, memory in gigabytes, and power drawn from the grid. The techniques used to shrink them — quantisation, pruning, knowledge distillation — produce models that are smaller, but structurally the same. They still require floating-point arithmetic. They still do not fit on a Cortex-M0.
Logic-Based Networks
Built for constraint
Logic-Based Networks (LBNs) are not neural networks made smaller. They use propositional logic — AND, OR, NOT — rather than matrix multiplication. Inference is a bitwise operation on integers, not a floating-point computation. Efficiency here is not an optimisation pass applied after training. It is the architecture. A model that would require a GPU-backed cloud API as a neural network runs on a $3 chip as an LBN.
faster inference
Arm Cortex-M4, MLPerf Tiny anomaly detection benchmark. LBN vs best neural network result on identical hardware.
less energy per inference
Same benchmark, same hardware. The operational lifetime difference on a battery-powered sensor is not incremental.
Edge AI examples: real-time intelligence in the field.
People search for edge AI examples because the concept is easier to grasp when you can see what it does in practice. The range is wider than most expect — from edge AI cameras classifying scenes in milliseconds to edge AI hearing aids processing audio without leaving the ear. What all these examples share is inference that happens on the device, without a cloud round-trip, at the moment it is needed.
Edge AI hearing aids
Hearing aids are among the most constrained edge AI devices in existence: a sub-milliwatt power budget, a millimetre-scale processor, and a hard real-time audio pipeline. LBNs run on exactly this class of hardware — classifying acoustic environments, suppressing noise, and adapting gain without a cloud API call or a battery drain that empties in hours.
Edge AI cameras
Vision-based edge AI requires fast, local classification of image or video data — in security systems, quality inspection lines, retail analytics, and traffic management. On-device processing means no video stream transmitted to a server, no latency waiting for a cloud response, and no ongoing inference cost compounding at scale across thousands of edge AI devices.
Predictive maintenance
Bearing degradation, pump cavitation, and motor faults detected from vibration and acoustic signals — classified on the sensor MCU in real time. The model runs on the same chip that drives the sensor assembly. No gateway. No cloud pipeline. No unplanned downtime.
IoT and smart sensors
Battery-operated field sensors classifying environmental conditions, detecting anomalies, or monitoring infrastructure — for years on a single cell. The intelligence lives at the node. Only classification results, not raw data streams, leave the device.
Wearables
Motion classification, health monitoring, and gesture recognition on body-worn devices. Wearables represent the intersection of size, power, and privacy constraints — where data must be processed locally, the battery lasts days, and the processor is a fraction of a fingernail.
Embedded control systems
Real-time classification feeding actuation decisions in automotive, industrial, and robotics applications. Deterministic output is a prerequisite for control systems — not a nice-to-have. LBNs provide it by design: same input, same output, every time.
Train once. Deploy on any hardware.
ModelMill trains and optimises models for on-device deployment across hardware from any maker. Its AutoML approach searches for the best-performing model within the constraints of the target platform — balancing accuracy, inference speed, memory footprint, and energy consumption without requiring the developer to specify any of these trade-offs manually. You tell it the hardware. It finds the model.
No Nvidia Jetson required. No specialised AI chip required. ModelMill produces edge AI that runs on whatever is already in your product.
Microcontrollers (MCU)
Arm Cortex-M0, M3, M4, and M7 families. STM32, Nordic nRF, NXP Kinetis, and Renesas RA series. Any 32-bit MCU can run an LBN — no FPU, no SIMD extensions, no specialist silicon required.
Neural processing units (NPU)
Where an NPU is present, ModelMill can target it for additional throughput gains. But unlike neural network frameworks that require NPU hardware, LBNs run efficiently on the CPU alongside — NPU availability is an optimisation, not a requirement.
Single-board computers
Raspberry Pi 4 and 5, BeagleBone, and industrial SBC platforms running embedded Linux. ModelMill generates portable C code with no framework dependencies — it runs without TensorFlow Lite, ONNX Runtime, or any ML library.
Edge AI chips and embedded systems
The market for purpose-built edge AI chips — from Silicon Labs, Ambiq, Maxim Integrated, and others — continues to grow. ModelMill's output is hardware-agnostic C code; it deploys on any chip that can run a C compiler, including every edge AI chip currently on the market and those not yet released.
What ModelMill delivers for every platform
A self-contained C SDK: the trained LBN, the inference engine, a build configuration for your target, and integration documentation. No runtime overhead. No ML framework dependency. No cloud connection at inference time. Drop it into your firmware and call two functions.
ModelMill: sensor data in, deployed model out.
ModelMill is the training platform for edge AI. It handles the complete workflow — from raw sensor data to a deployable LBN in a self-contained C SDK — without requiring ML expertise from the team doing the work. Upload labelled data. Specify the target hardware. Receive a model that fits.
Gather labelled sensor data: normal operation and the conditions you need to detect. Event-level labelling is sufficient — no sample-by-sample annotation required.
Upload to ModelMill. The platform constructs the training pipeline, searches the model space, and trains candidates against your labelled data and your specified hardware target.
ModelMill surfaces accuracy, inference latency, energy consumption, and model size for each candidate — benchmarked against your hardware profile. Select the model that fits your deployment constraints.
Package your selected LBNs as a packaged as an edge compatible self-contained C SDK: trained LBN, inference engine, build configuration, and documentation. No ML framework dependencies. No runtime overhead.
Integrate the SDK into your embedded firmware. The model runs on the target MCU alongside existing code. No cloud dependency. No ongoing inference cost. No network required at inference time.
Integrate the SDK into your embedded firmware. The model runs on the target MCU alongside existing code. No cloud dependency. No ongoing inference cost. No network required at inference time.
Train your first edge AI model.
ModelMill handles the full workflow end to end: labelled sensor data in, a complete deployment package out. No ML expertise required. No Nvidia Jetson. No cloud dependency.
Frequently asked questions
What is edge AI? 
Edge AI is artificial intelligence inference that runs directly on an edge device — a microcontroller, embedded processor, industrial computer, or other local hardware — rather than on a remote cloud server. The data is processed where it is generated. The decision is made locally and immediately. No network connectivity is required at inference time, and no raw data needs to leave the device. Compared to cloud AI, edge AI offers lower latency, lower power consumption, better privacy, and no ongoing inference cost after deployment. The trade-off is that the model must fit within the hardware constraints of the edge device — which is precisely what ModelMill is built to solve.
What is the difference between edge AI and embedded AI?
The terms are often used interchangeably, and the distinction is more one of framing than architecture. Embedded AI typically refers to inference running on dedicated embedded hardware — microcontrollers, system-on-chip devices, and purpose-built embedded systems — usually in a fixed, single-purpose deployment. Edge AI is a broader term that includes embedded devices but also encompasses edge servers, gateways, and any compute hardware deployed outside a data centre. In practice, both require small, efficient models that run without cloud connectivity. Logic-Based Networks trained through ModelMill serve both use cases.
What is the difference between edge AI and cloud AI?
Cloud AI sends data from the device to a remote server for inference; the result is returned over the network. Edge AI runs inference locally, on the device. The practical differences are latency (microseconds to milliseconds for edge versus tens to hundreds of milliseconds for cloud), connectivity dependency (none required for edge versus always required for cloud), data privacy (data stays on device for edge versus transmitted for cloud), power consumption (milliwatts for edge inference versus kilowatts for cloud GPU inference), and ongoing cost (none after deployment for edge versus per-inference cloud fees). For applications requiring real-time decisions, intermittent connectivity, long battery life, or data sovereignty, edge AI is not a preference — it is a requirement.
Does edge AI require specialist hardware like an Nvidia Jetson?
Not with Logic-Based Networks. The Nvidia Jetson — and similar GPU-equipped edge compute modules — exist because neural networks require GPU-level compute even after compression. LBNs use bitwise operations on integers rather than floating-point matrix multiplication, so they run on any 32-bit processor. Cortex-M0, Cortex-M4, ESP32, STM32, Raspberry Pi, and custom SoCs all work without modification. ModelMill trains models targeted at your specific hardware — no AI-specific silicon, no specialist accelerator, and no Nvidia Jetson required.
Can edge AI models run on a microcontroller?
Yes. Logic-Based Networks run on any 32-bit microcontroller, including Arm Cortex-M0 devices with no floating-point unit. The inference engine is portable C code with no external dependencies. At the MLPerf Tiny benchmark, the LBN anomaly detection model occupies 7.29 KiB of flash — well within the constraints of standard industrial and consumer MCUs. A model that would require a GPU-backed cloud API using a neural network approach runs on a $3 chip using an LBN trained through ModelMill.
What are the best edge AI models for constrained hardware?
For MCUs, embedded systems, and other power- and memory-constrained hardware, Logic-Based Networks are the most efficient model class available. At the MLPerf Tiny benchmark — the standard measure for tiny edge AI inference — LBNs run 54× faster and consume 52× less energy than neural network equivalents on identical hardware, whilst achieving comparable accuracy. For classification, anomaly detection, and sensor intelligence tasks, no other approach comes close on constrained hardware. ModelMill trains LBNs targeted at your specific device, automatically optimising for the balance of accuracy and efficiency your application requires.
Can ModelMill be used to create an edge AI model zoo for a hardware platform?
Yes — and this is one of the more compelling applications for hardware makers. A model zoo is a curated library of pre-trained models, optimised and ready to deploy on a specific hardware platform. Rather than requiring each customer to train from scratch, hardware makers can use ModelMill to build a collection of application-specific LBNs — for anomaly detection, vibration analysis, audio classification, keyword spotting, and more — each benchmarked and validated against the target chip. Customers then select from the model zoo, integrate the SDK, and deploy without touching the training pipeline. If you are a silicon vendor, module maker, or embedded platform provider interested in building a model zoo for your hardware, contact us to discuss.