Edge AI vs embedded AI

Edge AI

AI inference that occurs at or near the source of data generation, rather than in a centralised cloud. The "edge" is a network concept: it refers to the boundary between the physical world — where sensors and actuators live — and the computational infrastructure that processes what those sensors produce.

Edge AI spans a wide hardware range. A compact industrial server in a factory cabinet is edge AI. A gateway aggregating data from a sensor cluster is edge AI. An AI-capable camera analysing its own video feed is edge AI. A microcontroller classifying vibration data on a motor bearing is edge AI. The defining characteristic is location relative to the data source, not the hardware tier.

What is edge AI? covers the full picture of how edge inference works and where it applies.

Embedded AI

AI running on embedded systems: purpose-built, constrained hardware integrated directly into a product or device. Embedded systems typically operate with limited RAM (kilobytes to low megabytes), limited compute (no GPU, often no FPU on lower-end parts), a minimal RTOS or bare-metal firmware, and specific power budgets.

An embedded AI system is necessarily at the edge, but an edge AI system is not necessarily embedded. A ruggedised x86 server running inference in a factory cabinet is edge AI. It is not embedded AI: it has ample memory, a capable operating system, and essentially no constraint that would prevent running a standard neural network inference framework.

The relevant question for model selection is which category describes the actual deployment hardware. The answer determines whether neural networks are viable at all.

Hardware selection

The starting point for any edge AI project is the target hardware — or more precisely, the hardware that is already deployed and must not change. "Edge AI" as a category permits a wide range of hardware choices; "embedded AI" on constrained MCUs forces a narrow one.

A deployment targeting a capable gateway board — an Arm Cortex-A processor with 512 MB of RAM running embedded Linux — has access to standard neural network inference frameworks. TensorFlow Lite, ONNX Runtime, and similar tools work comfortably on this hardware. The model design space is wide.

A deployment targeting a production MCU — a Cortex-M4 with 256 KB of flash and 64 KB of SRAM — has a fundamentally different constraint set. The model design space is narrow. Neural networks, even after aggressive compression, often cannot fit. The design decision is not which framework to use; it is which class of model architecture can meet the hardware constraints at all.

Model design

Edge AI on capable hardware can accommodate quantised neural networks, tree-based models, and other approaches that assume access to meaningful memory and compute. The design question is about accuracy and latency trade-offs within a manageable space.

Embedded AI on constrained MCUs changes the question. Neural networks, even quantised to INT8, often exceed the available SRAM. The model cannot simply be made smaller without accuracy degrading to the point where it is no longer useful for the application. This is where the architectural choice between neural networks and Logic-Based Networks becomes practically significant: LBNs are designed for constrained embedded deployment from the outset, not compressed to fit it after training.

The memory footprint difference is not marginal. An LBN for a common anomaly detection task occupies a few kilobytes of flash. An equivalent neural network — even aggressively quantised — may require tens to hundreds of kilobytes. On a Cortex-M0 with 32 KB of flash, this is the difference between a model that deploys and one that does not.

Connectivity and autonomy

Edge AI systems are often connected. They may have a network link to a gateway, a cloud service, or other edge nodes. Connectivity enables periodic model updates, centralised monitoring, and event reporting. The system can tolerate occasional connectivity loss.

Embedded AI systems are frequently disconnected. Remote sensors, underground infrastructure, and vehicle ECUs may have intermittent or no network access during normal operation. The inference must be fully self-contained: no cloud call, no runtime dependency, no assumption that a network path exists when a decision needs to be made.

This changes the requirements for the deployment package. An embedded AI system needs a model and inference engine that runs entirely in local firmware, with deterministic output on any valid input, without any external dependency. A C-code SDK with no runtime library requirements satisfies this; a Python-based inference framework does not.

No GPU or NPU required

Inference runs on any standard 32-bit processor. This matters across the full edge-to-embedded range: from a Cortex-M0 sensor node where GPU silicon is not economically viable, to a capable edge server where GPU hardware adds cost and complexity that is unnecessary for the inference task.

Compact C-code SDK

The same SDK integrates into bare-metal firmware and Linux-based embedded environments. No runtime dependencies. No framework overhead. No dynamic memory allocation. The firmware team includes a header, calls a function, and the model runs.

Deterministic inference

The same input always produces the same output. Essential for embedded safety-critical applications where stochastic variation is unacceptable. Equally important for any application requiring certification under IEC 61508, ISO 26262, or similar standards — determinism substantially simplifies validation.

Energy-efficient inference

Energy efficiency matters across the spectrum, from coin-cell-powered remote sensors where it determines product viability to always-on edge servers where it affects operating cost. LBN inference draws substantially less energy per prediction than neural network approaches at every hardware tier. The battery-powered AI section covers the field-measured figures.