The intersection of edge computing and artificial intelligence constitutes one of today’s most dynamic technological fields. This area, known as Edge AI, combines the concept of processing and analyzing data near the point of generation with advanced AI models, helping businesses achieve real-time detection and response at the edge, while enhancing efficiency, agility, and security … Read more
Ten Influential Papers in the Field of Embedded AI Abstract Embedded AI is experiencing explosive growth, with significant breakthroughs in key technological directions such as model compression, edge computing, neural architecture search, and low-power algorithms from 2022 to 2025. This article selects ten influential papers that have not only won best paper awards at top … Read more
Sharing Cutting-Edge Research on TinyML 01 Definition TinyML, or “Tiny Machine Learning,” refers to the technology, tools, and methodologies for running machine learning applications on ultra-low-power microcontrollers (MCUs) and other small computing devices. The core of TinyML is to compress and optimize trained machine learning models to fit the limited memory (typically KB-level SRAM, MB-level … Read more
Generally speaking, Edge AI is a subset of conventional AI and should adhere to the same principles. However, when deploying artificial intelligence on edge devices, there are some special considerations that need to be taken into account. This article summarizes the differences between Edge AI and conventional AI based on the book AI at the … Read more
At three in the morning, your smartwatch suddenly vibrates—not as an alarm, but as a warning for abnormal heart rate. This small device independently completes the entire process from data collection to risk assessment without being connected to the internet. This is the magic of Edge AI—bringing AI down from the cloud to the terminal … Read more
Spanish AI startup Multiverse Computing has released two ultra-compact AI models, named “ChickBrain” and “SuperFly.” The company claims these are the world’s smallest high-performance models, capable of chat, voice, and reasoning. These models are designed to run locally on IoT devices, smartphones, and tablets without the need for an internet connection. The company employs quantum-inspired … Read more
Focus on model compression, low-bit quantization, mobile inference acceleration optimization, and deployment Abstract: The domestic epidemic situation has stabilized, and we are waiting for the open-source release of the Megvii framework at the end of March. Huawei Cloud has launched its 2020 “new flagship” – Kunpeng Cloud Phone. Meanwhile, the overseas epidemic is surging, and … Read more
Focus on model compression, low-bit quantization, mobile inference acceleration optimization, and deployment Introduction: This content includes 20 items. ARM has released the Cortex-M55 and Ethos-U55 series, suitable for voice AI model inference. ARM also published a white paper on deploying machine learning with the Cortex-M series and deploying convolutional network models with Cortex-M combined with … Read more
In the article AI System – 7 Pytorch Digital Recognition Practice and Operator Introduction, it mentions AI algorithms implemented using Pytorch on PC, and in AI System – 17 NPU Architecture Design Introduction, it discusses hardware implementation in NPU. Now, there is a question: How can AI algorithms be deployed and adapted on hardware? This … Read more
Focus on Model Compression, Low-Bit Quantization, Mobile Inference Acceleration Optimization, and Deployment Introduction: This issue contains 15 items. 【News】Shanghai Zhangjiang – News from several GPGPU companies: BoHan released cloud AI inference chip performance exceeding T4 with mass production expected in Q4 this year; Suipian released the largest AI chip in China, Birun’s first 7nm GPU … Read more