Real-time Inference

Real-Time Inference Detection of Industrial Defects (C++)

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Real-Time Inference Detection of Industrial Defects (C++)

Real-Time Inference Detection of Industrial Defects (C++)Source Code https://www.gitpp.com/robolao/project0828009-limr_cpp Introduction to the Open Source Project for Real-Time Inference Detection of Industrial Defects (C++) Project Background and Core Value In the context of industrial defect detection, edge computing devices have extremely stringent requirements for model inference speed. This project is based on the LiMR model (Lightweight … Read more

Miwen Power Adopts NVIDIA Jetson Thor to Usher in a New Era of Physical AI, Empowering Robots with Real-Time Inference

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Miwen Power Adopts NVIDIA Jetson Thor to Usher in a New Era of Physical AI, Empowering Robots with Real-Time Inference

At the prestigious World Robot Conference (WRC), Miwen Power’s BRD601 THOR carrier board served as the intelligent core of the Galbot G1 Premium, achieving a groundbreaking demonstration in collaboration with NVIDIA. The Galbot G1 Premium became the world’s first robot to deploy Jetson Thor internally, stunning the audience with its performance in completing industrial material … Read more

Liquid AI Launches Edge AI Model LFM2 for Millisecond Real-Time Inference and Offline Operation

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Liquid AI Launches Edge AI Model LFM2 for Millisecond Real-Time Inference and Offline Operation

According to a report from Electronic Enthusiasts (by Li Wanwan), the American startup Liquid AI has officially announced the launch of its next-generation Liquid Foundation Model (LFM2), which sets new records in speed, energy efficiency, and quality within the edge model category.The birth of LFM2 stems from a reconstruction of the underlying logic of AI. … Read more

Core Aspects of Edge AI Implementation: Hardware Selection and Model Deployment

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Core Aspects of Edge AI Implementation: Hardware Selection and Model Deployment

The implementation principle of Edge AI is to deploy artificial intelligence algorithms and models on edge devices close to the data source, enabling these devices to process, analyze, and make decisions locally without the need to transmit data to remote cloud servers. The goal of Edge AI implementation is to bring AI capabilities down to … Read more

Practical Edge AI with Python: From TinyML to NVIDIA Jetson for Edge Intelligence

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Practical Edge AI with Python: From TinyML to NVIDIA Jetson for Edge Intelligence

1. Evolution of Edge AI Technology From TinyML microcontroller-level inference to NVIDIA Jetson GPU-accelerated computing, the edge AI technology stack achieves a balance between computing power and power consumption. This tutorial covers the entire link of model lightweighting → real-time inference → offline deployment, focusing on solving core challenges such as model compression, hardware heterogeneity, … Read more