Applications of Edge Computing in Industrial IoT

Applications of Edge Computing in Industrial IoT

1968 Dick Morley invented the Programmable Logic Controller (PLC) and began deploying them in the automatic transmission manufacturing at General Motors, marking the era of precise control by controllers in various production stages of the manufacturing chain. 1975 Honeywell and Yokogawa Electric launched the world’s first Distributed Control Systems (DCS) TDC2000 and CENTUM, which allowed … Read more

Exploring The Future Of Autonomous Driving In Hangzhou

Exploring The Future Of Autonomous Driving In Hangzhou

⇧Click the blue text to follow “CCTV News” From April 25 to May 4, 2024, the Beijing International Auto Show will be held, featuring renowned domestic and international automotive brands showcasing their new products at a high standard. This auto show has specifically set up the “Smart Driving Future Exhibition Area”, where the word “smart” … Read more

TinyML: Making AI Lightweight with Edge Computing

TinyML: Making AI Lightweight with Edge Computing

Hello everyone, today I’m going to take you to explore a super interesting field – TinyML! Are you still worried about insufficient computing power of devices? Or are you concerned that AI models are too large to run? TinyML was born to solve these problems. It can make AI models lightweight, make smart devices smarter, … Read more

The Next Breakthrough in Machine Learning: TinyML

The Next Breakthrough in Machine Learning: TinyML

↑↑↑ Click on the blue text above, reply with materials, and get a surprise of 10GB Author: Mu Yang Source: Huazhang Computer (hzbook_jsj) Introduction: Today, we introduce a brand new version of machine learning that you may not have tried before, called TinyML. ML is something we are all familiar with, while TinyML is the … Read more

Learning Machine Learning with Syntiant TinyML

Learning Machine Learning with Syntiant TinyML

1. Self-Introduction and Board Introduction Hello everyone, I am “A Big Brother Rong”, and this time I am participating in the first issue of the second season of Funpack. The board used in this episode is the TinyML development board from Syntiant, a provider of deep learning solutions, which uses the NDP101 ultra-low power neural … Read more

TinyML: Unlocking New Paths for Microcontrollers in AI

TinyML: Unlocking New Paths for Microcontrollers in AI

TinyML is a miniature or small-scale artificial intelligence technology that can run on resource-constrained microcontrollers (MCUs) with features such as low latency, low power consumption, and low cost. It can perform inference tasks in AI such as keyword detection, anomaly detection, and object recognition. MCU Manufacturers Merging with AI Companies to Layout TinyML In May … Read more

TinyML: Implementing Machine Learning on Edge Devices

TinyML: Implementing Machine Learning on Edge Devices

Machine Learning (ML) is a vibrant and powerful field of computer science that permeates almost all digital devices we interact with, whether it’s social media, mobile phones, cars, or even household appliances. Artificial Intelligence (AI) is rapidly moving from the “cloud” to the “edge,” entering increasingly smaller IoT devices. The machine learning processes implemented on … Read more

Implementing Offline Command Recognition with TFLite Micro on ESP32

Implementing Offline Command Recognition with TFLite Micro on ESP32

1. Introduction Voice recognition, as an important method of human-computer interaction, is gradually becoming one of the core functions of smart devices. However, traditional voice recognition systems often rely on cloud servers for audio data processing and analysis, which brings issues such as latency and privacy. TensorFlow Lite provides an efficient and fast solution for … Read more

Efficient LLM Inference with Block Sparse Attention

Efficient LLM Inference with Block Sparse Attention

Click the card below to follow the “LiteAI” public account Hi, everyone, I am Lite. A while ago, I shared the Efficient Large Model Full-Stack Technology from Part 1 to Part 19, which includes content on large model quantization and fine-tuning, efficient LLM inference, quantum computing, generative AI acceleration, etc. The content links are as … Read more