AI Smart Safety Helmet: Passive to Active Protection

Overview of AI smart safety helmet integrating AI vision, vital-sign and environmental sensors for real-time monitoring, alerts, positioning and intelligent management.

What is an AI Edge Compute Box?

Comprehensive overview of an AI edge compute box: definition, operation, features, security, and industry use cases for real-time edge computing and computer vision.

Parallel Computing and Acceleration Techniques for Neural Networks

Overview of parallel computing and acceleration for neural networks, covering data/model parallelism, GPU/TPU and software optimizations like mixed precision.

Why A100/H100 Beat RTX 4090 for Large-Model Training

Technical analysis comparing RTX 4090 and H100 GPUs: why the 4090 is impractical for large-model training but viable for inference with optimized batching and KV cache.

How CNNs Solve Edge Detection

Overview of convolutional neural networks: principles like padding, stride, pooling and filters, edge detection fundamentals, architecture patterns and a Keras MNIST implementation.

AI and Machine Learning on Low-Power Microcontrollers

Technical overview of TinyML on MCUs: frameworks, model optimization (quantization, pruning), accelerators, toolchains, and deployment guidance for embedded systems.

Deepseek R1 Offline Deployment Guide

Technical guide to installing RKLLM-Toolkit and converting/deploying the DeepSeek-R1 LLM on EASY-EAI-Orin-Nano (RK3576), covering env setup, conversion, and on-device inference.

Efficient Deployment of DeepSeek on Intel Platforms

Guide to deploying the DeepSeek LLM on Intel Arc platforms: hardware, BIOS, Ubuntu 24.10, Arc B580 drivers and OpenVINO setup to validate model inference.

RDK X3 Tennis Ball Collector Robot

Autonomous tennis-ball collection and serving robot using YOLOv5 detection and SLAM/AMCL localization, with optimized path planning and hierarchical wheel control.

Deploy DeepSeek-R1 on RK3588 Development Board — Part 1

Technical guide to porting DeepSeek-R1 onto RK3588-based edge hardware: model conversion, cross-compilation, board deployment, and measured CPU, memory, and NPU performance.