Single-board computers, with their high cost-effectiveness, miniaturization, and ease of development, are the core carriers for AI prototyping and edge deployment. They achieve this through a combination of computing power matching scenarios, algorithm adaptation to hardware, and peripheral device integration, covering all scenarios for lightweight AI inference.

I. Core Prerequisite: Matching Single-Board AI Capabilities (Computing Power Determines the Upper Limit of Scenarios)

The core of single-board AI applications is "inference deployment" (training requires cloud/high-performance GPU). Computing power depends on CPU clock speed, integrated NPU/GPU/ISP, and memory bandwidth. Select the appropriate model according to the scenario:

1. Lightweight AI Scenarios (Entry-Level)

Computing Power Requirements: CPU ≥ 4-core Cortex A53, no dedicated NPU (relies on CPU software inference), Memory ≥ 2GB

Suitable Scenarios: Image classification (e.g., object recognition), simple voice wake-up, sensor data AI analysis (e.g., anomaly detection)

Recommended Models: Raspberry Pi 4B (4-core A72@1.5GHz), Orange Pi 5 (4-core A55@2.4GHz), ESP32S3 (dual-core LX7, low-power edge sensing)

2. Medium-Heavyweight AI Scenarios (Advanced)

Computing Power Requirements: Integrated dedicated NPU (computing power ≥ 1 TOPS), CPU ≥ 4-core Cortex A76/A55, Memory ≥ 4GB LPDDR4

Suitable Scenarios: Object detection (e.g., person/vehicle recognition), image segmentation, real-time speech-to-text, lightweight edge AI gateway

Recommended Models: Raspberry Pi 5 (VideoCore VII GPU + 1 TOPS NPU), Rockchip RK3588 single-board computer (6 TOPS NPU, supports 8K video AI analysis), NVIDIA Jetson Nano (128-core GPU, 472 GFLOPS, powerful AI acceleration)

3. Industrial-grade AI Scenarios (Professional Grade)

Computing Power Requirements: NPU computing power ≥ 5 TOPS, supports industrial interfaces/wide temperature range, strong anti-interference

Suitable Scenarios: Industrial visual inspection (e.g., product defect recognition), vehicle AI perception, industrial edge AI computing nodes

Recommended Models: NVIDIA Jetson Xavier NX (21 TOPS computing power), Advantech UNO220 series (industrial grade, supports AI acceleration module expansion), NXP i.MX 8M Plus single-board computer (2.3 TOPS) NPU (Wide Temperature Range 40℃~85℃)

II. Core Process of Single-Board Computer AI Application (4 Steps to Implementation)

1. Hardware Selection and Peripheral Matching (Scenario-Driven)

Core: Select a single-board computer based on the AI task (e.g., for real-time video AI, a model with NPU+ISP is essential), and match it with corresponding peripherals for data acquisition.

Common Peripherals:

Image/Video: USB camera (entry-level), MIPI industrial camera (professional visual inspection), infrared camera (nighttime scenes)

Voice: Microphone array (noise reduction, speech recognition), speaker (speech synthesis output)

Data Acquisition: Sensors (temperature, humidity/vibration/infrared, for anomaly analysis in conjunction with AI), industrial bus modules (RS485/CAN, for data interface in industrial scenarios)

Actuation: Servo motors/motors (for controlling equipment actions after AI decision-making, such as intelligent robots), relays (for controlling home appliances/industrial equipment)

2. Software Environment Setup (AI Development Fundamentals)

System Selection: Prioritize Linux distributions compatible with AI frameworks (e.g., Raspberry Pi for Raspberry Pi) OS, Jetson uses the JetPack system, which comes with an AI acceleration library)

Core tools/frameworks:

Lightweight beginner: Python + OpenCV (image preprocessing) + TensorFlow Lite/PyTorch Mobile (lightweight AI model deployment, adapted to single-board computers with low computing power)

Advanced acceleration: NVIDIA CUDA/CuDNN (Jetson series GPU acceleration), Rockchip RKNN Toolkit (RK series NPU model conversion and deployment)

Development assistance: VS Code remote debugging, Jupyter Notebook (quick code verification), MQTT protocol (AI analysis results uploaded to the cloud/linked with other devices)

3. AI model adaptation and deployment (core link)

(1) Model selection: Prioritize lightweight pre-trained models (adapted to single-board computer computing power)

Image: MobileNet (classification), YOLOv8n (lightweight object detection, suitable for real-time operation on single-board computers), UNetLite (image segmentation)

Speech: Whisper Tiny (lightweight speech-to-text), Snowboy (voice wake-up, low power consumption)

Data: LightGBM/XGBoost (lightweight structured data AI analysis, such as sensor anomaly detection)

(2) Model Optimization: Reduce computing power consumption and adapt to single-board computers

Model Compression: Trim redundant parameters, quantize (e.g., convert 32-bit floating-point numbers to 8-bit integers, reducing computing power consumption by 75%, with controllable accuracy loss)

Framework Conversion: Convert trained models (e.g., TensorFlow/PyTorch) to formats supported by single-board computers (e.g., TensorFlow Lite → Raspberry Pi, RKNN → Rockchip, ONNX → Jetson)

(3) Model Deployment: Code execution

Core Logic: Peripheral data collection (e.g., camera images) → Data preprocessing (OpenCV trimming/denoising) → Loading the optimized AI model → Inference analysis (output results, such as "2 people detected") → Decision execution (e.g., triggering alarms, controlling equipment) Example (Beginner): Raspberry Pi 4B + USB camera + YOLOv8n for real-time object detection (Core code: Camera captures frames → YOLOv8n inference → OpenCV annotation and display of detection results)

4. Debugging, Optimization, and Deployment (Ensuring Stable Operation)

Performance Optimization: If inference is laggy, reduce video resolution (e.g., 1080P → 720P), reduce model input size, close unnecessary background processes, and prioritize NPU/GPU acceleration (avoid pure CPU software inference).

Stability Debugging: Industrial scenarios require high/low temperature/interference testing; optimize data transmission latency (e.g., prioritize local inference, reduce cloud dependency).

Scenario Deployment: Set the program to start automatically on boot, connect to cloud platforms (e.g., Alibaba Cloud/Huawei Cloud) to upload AI analysis results, or link with local devices to achieve closed-loop control (e.g., AI detects a fire → triggers an alarm + cuts off power).

III. Typical AI Application Scenarios (with Hardware + Core Solution)

1. Intelligent Security Monitoring (Easy to Get Started and Deploy)

Hardware: Raspberry Pi 5 + USB HD Camera + Buzzer

Core Solution:** Deploy a YOLOv8n object detection model to identify "strangers/abnormal objects" in real-time. Upon detection, a buzzer alarm is triggered, and an image is captured and uploaded to the cloud.

Advantages: Low cost (total cost ≤ 1500 RMB), short development cycle (12 days)

2. Industrial Product Defect Detection (Professional Grade)

Hardware: Jetson Xavier NX + MIPI industrial camera + industrial relay module

Core Solution: Collect product image datasets → train a lightweight defect detection model (e.g., YOLOv8s quantized version) → deploy to Jetson, capture real-time images of products on the production line, detect defects (e.g., scratches/deformation) → trigger a relay to control the production line to pause, and simultaneously mark the defective product.

Advantages: Inference latency < 50ms, adaptable to real-time industrial production needs, strong anti-interference capability

3. Intelligent Voice Assistant (Consumer Grade)

Hardware: Orange Pi 5 + microphone array + speaker

Core Solution: Deploy the Snowboy voice wake-up model (customizable wake-up word, e.g., "Little Orange Assistant") + Whisper Tiny speech-to-text model + ChatGLM6B quantized version (lightweight dialogue model) achieves a closed loop of "wake-up → voice command recognition → AI dialogue response → speech synthesis output".

Advantages: Primarily local inference, fast response, basic use even without network.

4. Edge AI Data Gateway (IoT Scenarios)

Hardware: RK3588 single-board computer + LoRa communication module + sensors

Core Solution: Sensors collect temperature, humidity/equipment vibration data → Single-board computer analyzes data locally using AI model (e.g., identifying abnormal vibration = equipment failure) → Normal data is periodically uploaded to the cloud, abnormal data is pushed with real-time alarms, and LoRa control is used to shut down and maintain on-site equipment.

Advantages: Edge-side AI preprocessing reduces cloud computing power pressure, suitable for remote industrial scenarios without network.

IV. Selection and Development Pitfalls

1. Don't blindly pursue high computing power: Choose Raspberry Pi/Orange for lightweight scenarios. 1. For heavy AI applications, choose Jetson/RK3588 to avoid excessive performance and wasted costs;

2. Prioritize hardware acceleration: Single-board NPU/GPU acceleration must be enabled. Pure CPU software inference has high latency and cannot support real-time scenarios;

3. Prioritize lightweight models: Avoid directly deploying large AI models (such as GPT3). Prioritize tiny/nano-level lightweight models or perform quantization compression;

4. Confirm peripheral compatibility: Before selection, confirm that the single-board computer supports peripheral interfaces (e.g., MIPI cameras require a single-board computer with the corresponding interface) to avoid hardware incompatibility;

5. Choose industrial-grade models for industrial scenarios: Ordinary consumer-grade single-board computers (such as Raspberry Pi) lack wide-temperature/interference resistance designs. For industrial scenarios, Jetson industrial edition/Advantech and other brand-name models are essential.