Model Quantization

Design and Development of a Voiceprint Recognition System Based on Embedded Systems and Deep Learning

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Design and Development of a Voiceprint Recognition System Based on Embedded Systems and Deep Learning

Introduction Voiceprint recognition technology, as an important branch of biometric recognition, is rapidly gaining popularity in fields such as smart access control, voice assistants, and identity authentication due to its non-contact nature, ease of collection, and high security. With the integration of embedded systems and deep learning technology, it has become possible to deploy high-performance … Read more

When LLM Meets Hardware: Unlocking the Next Productivity of Embedded AI

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When LLM Meets Hardware: Unlocking the Next Productivity of Embedded AI

【Have you ever thought about?】 A palm-sized development board can run its own privatized large model Industrial equipment can provide real-time feedback on fault sources through natural language Local computing power in smart homes can also understand your vague commands We are entering a new era—LLMs (Large Language Models) are no longer just “behemoths” in … Read more

Innovative Energy-Saving NPU Technology Developed by South Korean Researchers Reduces AI Model Power Consumption by 44%

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Innovative Energy-Saving NPU Technology Developed by South Korean Researchers Reduces AI Model Power Consumption by 44%

Researchers at the Korea Advanced Institute of Science and Technology (KAIST) have developed an energy-saving NPU (Neural Processing Unit) technology that has shown significant performance improvements in laboratory tests. Based on controlled experimental results, their dedicated AI chip runs AI models 60% faster than the graphics cards currently powering most AI systems, while reducing power … Read more

Top Ten Edge AI Chips

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Today, artificial intelligence is permeating almost all edge and embedded markets, creating devices with more powerful performance and richer functionalities, ranging from predictive maintenance in industrial machines to voice activation in household appliances, and supporting more complex computer vision applications and autonomous machines. Generative Artificial Intelligence(GenAI) is also rising in edge devices, enabling these devices … Read more

Getting Started with NPU: Essential Modules for Learning

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Getting Started with NPU: Essential Modules for Learning

With the current popularity of AI chips, many IC engineers are eager to learn about NPU (Neural Processing Unit) but are unsure where to start. This article will discuss various dedicated modules for AI chips, providing some direction for your learning. Dedicated Modules for AI Chips 1. NPU Core Module (Neural Processing Unit) Definition: A … Read more

TinyML: A Python Library for AI Deployment on Micro Devices!

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TinyML: A Python Library for AI Deployment on Micro Devices!

MarkDown # Getting Started with TinyML: Playing with AI on Micro Devices Using Python Hello everyone! Today we are going to explore a super cool Python library – TinyML. In simple terms, TinyML is a magical tool that allows AI models to run on micro devices like smartwatches and sensors. Imagine your fitness band intelligently … Read more

Guide to Deploying Lightweight AI on STM32: Making Microcontrollers “Smart” with TinyFlow

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Guide to Deploying Lightweight AI on STM32: Making Microcontrollers "Smart" with TinyFlow

This guide covers hardware selection, model optimization, toolchain operations, code implementation, and debugging techniques, using the STM32 series microcontrollers as an example: 1.Hardware Selection and Configuration (1)Clarify Requirements Computational Requirements: Simple classification tasks (e.g., binary classification of sensor data):Cortex-M0+/M3 (e.g., STM32G0/F1) are sufficient. Complex tasks (image recognition, speech processing): Choose models with hardware acceleration (e.g., … Read more

Comprehensive Analysis of ADC Interfaces in Embedded Education

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Comprehensive Analysis of ADC Interfaces in Embedded Education

In the contemporary information technology system, embedded system interfaces serve as the core infrastructure for data exchange, forming the neural hub for device interconnection. Based on standardized communication protocols and interface specifications, the technical architecture enables efficient data exchange and intelligent collaborative operations among heterogeneous devices. This article selects the Analog-to-Digital Converter (ADC) interface as … Read more

Performance Optimization Methods for C++ Deployment

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Performance Optimization Methods for C++ Deployment

01 Use Structures to Store Common Variables in AdvanceWhen writing preprocessing and postprocessing functions, certain variables, such as the shape of the model input tensor and count, are often used multiple times. If these values are recalculated in each processing function, it will increase the computational load during deployment. In such cases, consider using a … Read more

FBGEMM: A Remarkable C++ Library for Efficient Matrix Operations

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FBGEMM: A Remarkable C++ Library for Efficient Matrix Operations

FBGEMM (Facebook General Matrix Multiplication) is a C++ library developed by Meta (Facebook) that is primarily used for low-precision, high-performance matrix multiplication and convolution operations in server-side inference. It is designed for small batch data and can significantly improve inference efficiency while supporting various techniques to reduce precision loss, such as row-wise quantization and outlier-aware … Read more