Embedded AI explained
Author
Débora
Published
Embedded AI refers to an artificial intelligence solution that runs entirely on the device that hosts and uses it: a camera, a sensor, a machine, a vehicle. No remote server, no data exchange with any other remote hardware. Definition, hardware involved, use cases, development requirements and the difference with Edge AI: here are the essentials to understand this technology and know when to use it.
Embedded AI defined - AI that runs directly inside your equipment
Embedded AI is artificial intelligence built directly into a piece of equipment: an inspection camera, a machine, a sensor, a robot, a vehicle. The equipment analyzes what it captures by itself and makes decisions on its own, without connecting to a server or to any other hardware.
For a business, this changes three things: the equipment works even without a network, it reacts immediately, and its data stays on site.
Cloud AI and embedded AI: where does the computing happen?
Most off-the-shelf AI solutions run in the cloud. The data (an image, a measurement, a document) travels over the Internet to a data center, where powerful servers analyze it before sending back the result. The equipment in the field simply sends and receives.
Embedded AI places the AI model inside the equipment itself. The equipment does not rely on any other hardware: no remote server, not even a computer installed three meters away in the same workshop. The result of the analysis also stays on site and is used directly to take action.
On a production line, for example, an inspection camera with embedded AI spots a defective part and triggers its ejection itself, without waiting for a response from a server.
Where computing happens
A remote data center for cloud AI, the equipment itself for embedded AI.
Internet connection
Essential for cloud AI, unnecessary for embedded AI.
Data
It travels over the network with cloud AI and never leaves the equipment with embedded AI.
Computing power
Very high in the cloud, limited to the equipment's own capacity when embedded.
What is embedded AI used for? Situations and main use cases
Embedded AI becomes relevant as soon as relying on the cloud raises an issue of connectivity, latency, confidentiality, data volume or energy. In most projects, several of these constraints add up.
Five situations where embedded AI is the right choice
- No reliable connection.
Remote sites, dead zones, basements, maritime or airborne environments, mobile equipment: the device must work whatever the network coverage. - Critical response time.
When a decision must be made within a few milliseconds (sorting objects on a fast conveyor, avoiding an obstacle, etc.), a round trip over the network is a risk. - Data that must not be exposed.
Images of people, medical data, manufacturing know-how: on-device processing limits data exposure, ensures confidentiality and simplifies GDPR compliance. - Too much data to transmit.
A camera filming continuously produces a stream that is costly to send around the clock. The device analyzes the images on site and keeps only the useful information. - Battery life to preserve.
On a battery-powered sensor, network transmission is among the most power-hungry operations. Analyzing locally and transmitting only when an anomaly occurs extends battery life.
Main fields of application
- Industry: in-line visual quality control, predictive maintenance through vibration analysis, operator safety around machines.
- Mobility and robotics: driver assistance, drones, autonomous mobile robots.
- Healthcare: wearable medical devices that analyze a physiological signal without transmitting it.
- Agriculture: a precision weeding machine continuously analyzes images of the field to tell crops from weeds. This video stream would be too heavy to transmit, often from plots with poor network coverage.
What these uses have in common: the device decides on its own, as close to the data as possible.
What hardware can run embedded AI?
Embedded AI runs on any device whose processor and memory can execute a model, from a battery-powered sensor to an autonomous robot. The choice of hardware depends on three constraints: computing power, available memory and energy consumption.
Three main families of hardware
- Microcontrollers. These are small, very low-power chips found in household appliances, industrial sensors and connected devices. Their RAM is often measured in hundreds of kilobytes: less than the size of a single smartphone photo. They run very lightweight models (TinyLM), such as recognizing a voice keyword or detecting an abnormal vibration.
- Embedded processors with an AI accelerator. The chips in smartphones, smart cameras and many industrial units include a circuit dedicated to AI computing, the NPU (Neural Processing Unit). They process images or video in real time.
- High-performance computing modules. These compact boards include a GPU and are fitted in robots, drones and vehicles. They run heavier models, or several models in parallel across multiple cameras.
For technical readers:
From the ESP32 microcontroller costing a few euros to AMD FPGAs, by way of Hailo accelerators or NVIDIA Jetson modules, the choice of target can depend on several factors, such as the hardware already in place, unit cost, footprint, power consumption or expected response time.
Embedded code: converting an AI model so it runs on the device
An AI model is usually designed and trained on very powerful servers. To run inside a piece of equipment, it must be adapted: slimmed down, translated into the language of the chip that will host it, then tested on the final hardware. This adaptation work is what produces embedded code.
The difficulty lies in the gap between the two worlds. A server has abundant memory and energy; an embedded chip sometimes has thousands of times less. Each chip family also has its own tools and rules: the same model is adapted differently depending on the equipment it runs on.
Steps to reliable embedded code
- Define the equipment's constraints: available memory, expected response speed, power consumption, required level of reliability.
- Choose a suitable model: some AI models are designed from the outset to run on small devices.
- Slim down the model: reduce its size and the precision of its calculations, while staying within an acceptable margin of error.
- Translate it for the target chip: use the manufacturer's tools to produce a program the equipment can execute.
- Integrate it into the equipment: connect it to the sensors and mechanisms it controls, preparing the data exactly as during training.
- Validate it on the actual hardware: check that it gives the same results as the original model, measure its speed and power consumption, and test it in real field conditions.
- Plan for updates: organize how the model will evolve once the equipment is deployed.
Points to watch
What can go wrong without AI expertise
A model incompatible with the hardware
This is often the main difficulty, especially on less common chips. An AI model chains together computing operations, organized in layers, and each chip can only execute some of them. The engineer must therefore choose a model whose architecture is compatible with the target hardware, then convert the incompatible layers where possible. Without this work, the model does not run on the equipment, or runs poorly.
Performance validated only in the lab
Dust, vibrations, changes in lighting or temperature change everything in the field. The model can also end up frozen, hard to maintain over time. Without a way to update it, the equipment can no longer keep up with changes in products or uses.
Poorly prepared data
If images or measurements are not presented to the AI exactly as during its training, its results degrade, without any error message.
An AI that is too slow
Some of the computing runs outside the circuit designed for AI, and the response time becomes incompatible with the use case. In addition, a lack of memory causes crashes or reboots that are difficult to diagnose once the equipment is in service.
Chip families, manufacturer-specific tools, slimming techniques: the options are many, and each choice shapes the next. A solution that works in a demo can prove unsuitable once the equipment is in production. A specialist who knows these options and their limits is essential to pick the right ones, then implement them correctly. Without this expertise, several pitfalls lie in wait...
These failures have one thing in common: they trigger no alert. The equipment keeps running, with reduced reliability. This work therefore calls for engineers who master both AI and hardware constraints.
Embedded AI or Edge AI: what's the difference?
Edge AI refers to AI that runs close to the data source, on local equipment separate from the sensor: an industrial computer, a gateway, an on-site server.
As soon as data leaves the device that captures it to be processed elsewhere, even three meters away, it is edge. Ten cameras connected to an industrial PC with a GPU in the workshop: that's edge. A camera that analyzes its images "by itself": that's embedded.
Embedded AI is its most advanced form, since the computing takes place in the very device that captures the data. That said, the term "Edge AI" is often used in a broad sense that includes embedded AI.
When is edge a good option?
- Several sources to analyze together: correlating images from multiple cameras or measurements from multiple sensors.
- A model too heavy for the sensor: the local equipment provides computing power the device cannot carry on board.
- Shared computing power: instead of fitting each of a workshop's twenty cameras with its own AI chip, a single server analyzes all twenty streams. There is less hardware to buy.
- Centralized updates: the model is updated in one place, without intervening on each device.
- Data that stays on site: edge retains the sovereignty and low-latency advantages over the cloud, without depending on the Internet.
On the other hand, the system depends on the local network, and the central equipment becomes a single point of failure that must be secured.
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