List of Open-Source Inference Engines for TinyML MCUs

Open-Source Inference Engines Currently, the mainstream and active open-source TinyML inference engines with over 1k stars on GitHub provide core support for implementing neural network model inference on MCUs. Arm CMSIS-NN/DSP (CMSIS-6) A function library designed specifically for Arm Cortex-M cores, providing efficient neural network (NN) and digital signal processing (DSP) core functions. https://github.com/ARM-software/CMSIS_6 Google … Read more

The Integration of TinyML and LargeML: A Review for 6G and Beyond

Abstract—The evolution from 5G to 6G networks highlights the strong demand for Machine Learning (ML), particularly for Deep Learning (DL) models, which have been widely applied in mobile networks and communications to support advanced services in emerging wireless environments such as smart healthcare, smart grids, autonomous driving, aerial platforms, digital twins, and the metaverse. With … Read more

Enhancing Reasoning and Control Capabilities: Breakthroughs in the Dual-System VLA Model for Embodied Robots

FiS-VLA Team SubmissionQuantum Bit | WeChat Official Account QbitAI Teaching robots to execute tasks intelligently, quickly, and accurately has always been a challenge in the field of robotic control. To address this issue, The Chinese University of Hong Kong, Peking University, Zhi Square, and the Beijing Academy of Artificial Intelligence have collaboratively proposed the Fast-in-Slow … Read more

Soft Reasoning: An Efficient Inference Paradigm for Large Language Models

MLNLP community is a well-known machine learning and natural language processing community both domestically and internationally, covering NLP graduate students, university professors, and corporate researchers.The vision of the community is to promote communication and progress between the academic and industrial sectors of natural language processing and machine learning, especially for beginners. Paper Title: Soft Reasoning: … Read more

Edge AI vs Cloud AI: Which is Better for Enterprise Workloads?

Introduction: With the widespread adoption of AI technology, enterprises face a critical question: Where should AI processing take place—on edge devices or in the cloud? This article will delve into the main differences between Edge AI and Cloud AI, their respective advantages, and how enterprises can make choices in different scenarios, even combining both for … Read more

Revolutionizing the Robotics Industry Ecosystem with Edge AI

Weekly Summary Every ThursdayPERSONAL WORK SUMMARYThisIssueTopicEdge AIIntroduction: Advantech’s AS&R Solutions Empowering Innovation in the Robotics Industry. The global manufacturing industry is currently undergoing a profound transition from “automation” to “intelligence,” with robotics technology having become the “new focus” of international competition. According to data from the International Federation of Robotics (IFR), the global installation of … Read more

High Power Density Power Module for Edge AI Applications

In modern AI data centers and edge computing environments, high power density and integration are mandatory requirements. To meet the demand for efficient power management solutions, Microchip Technology has launched the MCPF1412 power module, a highly integrated Point of Load (POL) buck converter featuring I2C and PMBus® interfaces, compact size, system telemetry, and protection functions, … Read more

Analysis of Edge AI Box Technology: ASIC/FPGA/GPU Chips and Edge-Cloud Collaboration with Adaptive Inference

Comprehensive report from Electronic Enthusiasts Network, the Edge AI box is a hardware device that integrates high-performance chips, AI algorithms, and data processing capabilities, deployed at the edge of data sources such as factories, shopping malls, and traffic intersections. It can perform local data collection, preprocessing, analysis, and decision-making without the need to upload all … Read more

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

Counter-Unmanned Aircraft Systems (CUAS) Network and Sensor Simulation

Autonomy in systems has been a development goal since the inception of computers. Today, code equipped on websites can assist in self-writing, while autonomous vehicles are continuously optimizing their safety and endurance to gain public trust. Quadcopters can autonomously follow their owners and deliver packages, minimizing the need for human intervention. Although these advancements seem … Read more