What Exactly Is the Difference Between CPU, GPU and NPU? A Complete Guide to Embedded, Industrial Control and Selection

Anyone doing embedded development, tinkering with industrial control boards or debugging on-device AI has almost certainly wrestled with the same question:

All are chips, so why do some boards win on CPU stability, others on GPU display, and others on NPU AI computing power?

How exactly do the three divide the work?

How should different projects choose a chip, and how do you avoid the pitfalls?

This article skips the jargon. Using plain language and real measured cases from industrial control boards, together with the full Rockchip platform range, it walks you through the essential differences between CPU, GPU and NPU — so you can choose confidently, avoid pitfalls and not waste budget.

1. A plain breakdown: the core division of labour between CPU / GPU / NPU

1. CPU: the device’s “master brain and supreme commander”

The CPU’s strength is not raw speed, but flexibility, judgement and stable scheduling.

It consists of a small number of high-performance large cores and focuses on serial logic operations, complex decisions and real-time task scheduling.

Its main responsibilities:

• System operation and process management

• Peripheral control, serial/network/IO scheduling

• Protocol parsing, data exchange, human-machine interaction

• Industrial real-time control, device status management

Weakness: it is not suited to large batches of simple, repetitive parallel operations, nor to pure AI inference and large-scale image rendering.

2. GPU: a large-scale parallel “graphics and computing accelerator”

The GPU is the opposite of the CPU: it has a huge number of simple cores, well suited to doing thousands of repetitive tasks at once.

Originally built for image rendering alone, it is now a general-purpose parallel computing unit supporting video codecs, matrix operations and parallel vision processing.

Its main responsibilities:

• Screen rendering, independent multi-screen output, UI drawing

• 4K/8K hardware video encoding and decoding

• Image preprocessing, scaling and stitching

• General-purpose parallel matrix computation

Misconception corrected: a GPU can run AI, but it is not architected for AI and is far less efficient than a dedicated NPU.

3. NPU: a dedicated “neural network computing core” built for AI

Many people assume the NPU is simply “a stronger GPU” — this is the biggest selection mistake of all.

The GPU is a general-purpose parallel unit; the NPU is a hard-wired neural network architecture optimised specifically for convolution, pooling, activation functions and matrix inference.

Core NPU advantages:

• Extremely high AI inference energy efficiency and lower power consumption

• High compute density: models run faster and generate less heat

• Supports quantised inference and concurrent multi-channel video AI

In short: rendering relies on the GPU, AI relies on the NPU.

4. Today’s mainstream embedded architecture: heterogeneous collaboration

Today’s mainstream Rockchip industrial SoCs all use a CPU + GPU + NPU heterogeneous computing architecture:

✅ CPU coordinates scheduling and keeps the device stable

2. Hardware cases: understanding the real differences between chips

1. CPU-focused stability: the first choice for industrial control and IoT (RK3568)

Corresponding hardware: Rocktech MTB-720B motherboard

Configuration: quad-core A55 at 2.0 GHz, NPU 1 TOPS (lightweight AI only)