AI, Machine Learning, and Edge AI: Concepts and Applications
Overview of machine learning paradigms, deep learning fundamentals, and Edge AI deployment—advantages, use cases, and implications for low-latency, on-device processing.
Overview of machine learning paradigms, deep learning fundamentals, and Edge AI deployment—advantages, use cases, and implications for low-latency, on-device processing.
Technical overview of the iW-RainboW-G58M SoM with Intel Agilex 5 FPGA for edge AI, covering AI tensor acceleration, software toolchain and lifecycle support.
RK3576-based AI gateway for power station buildings: hardware selection, interfaces (UART, Ethernet, video), Linux performance and multi-sensor fault monitoring.
Technical overview of microprocessors in AI: core roles, application cases (edge, autonomous vehicles, smart manufacturing), key challenges (power, data, security) and trends.
Edge AI and vision sensors overview: embedded ML platforms, real-time image processing, depth sensing, and on-device inference for industrial, smart city, and IoT applications.
Overview of Analog Devices' MAX78002 MCU with integrated CNN accelerator for edge AI: architecture, low-power SIMO/DVS power management, interfaces, and EV kit.
Human keypoint detection guide covering API, model deployment, example code, 17-point keypoint mapping and benchmark runtime (53–93 ms) on EASY-EAI Orin-Nano.
Technical overview of differential scanning calorimeter capabilities and selection: measuring polymers, metals, chemical kinetics, pharma and food stability, and DZ-DSC models.
MambaQuant: PTQ for Mamba models using KLT-enhanced and smoothed fused rotations to enable high-accuracy W8A8/W4A8 post-training quantization with <1% loss.
Step-by-step deployment guide for Janus-Pro on Orange Pi AI Pro (20T) using MindSpore v2.5.0 and MindSporeNLP, covering environment setup, swap, and examples.