Quantization

Efficient Fine-Tuning Methods for Quantized Large Models: QLoRA

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Efficient Fine-Tuning Methods for Quantized Large Models: QLoRA

Paper Title: QLoRA: Efficient Finetuning of Quantized LLMs Authors: Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, Luke Zettlemoyer Project Address: https://github.com/artidoro/qlora Author: Jay Chou from Manchester Reviewer: Los Abstract: QLoRA is a model quantization algorithm proposed by Tim Dettmers from the University of Washington, applied in LLM training to reduce memory requirements. It is sufficient to … Read more

Guide to Calculating GPU Memory Requirements for LoRA and QLoRA Fine-Tuning: Understandable for Beginners

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Guide to Calculating GPU Memory Requirements for LoRA and QLoRA Fine-Tuning: Understandable for Beginners

I have recently compiled a simple and easy-to-understand guide on the GPU memory requirements for fine-tuning with LoRA and QLoRA, which can help you estimate the memory needed when fine-tuning using LoRA and QLoRA. Below, we will explain step by step, requiring minimal background knowledge.1. What are LoRA and QLoRA? LoRA (Low-Rank Adaptation):This is a … Read more

New Breakthrough in TinyML! Efficient Indoor Localization Using Transformers and Mamba

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New Breakthrough in TinyML! Efficient Indoor Localization Using Transformers and Mamba

Paper Title: Optimising TinyML with Quantization and Distillation of Transformer and Mamba Models for Indoor Localisation on Edge DevicesPublication Date: December 2024Authors: Thanaphon Suwannaphong, Ferdian Jovan, Ian Craddock, Ryan McConvilleAffiliations: University of Bristol, University of AberdeenOriginal Link: https://arxiv.org/pdf/2412.09289Open Source Code and Dataset Link: https://github.com/AloeUoB/tinyML_indoor_localisation Introduction Typically, accurate indoor localization systems rely on large machine learning … Read more

Analyzing ADC Working Principles Using Multisim

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Analyzing ADC Working Principles Using Multisim

Today, I would like to analyze the working principles of ADC. 1. Analog to Digital Conversion Analog to Digital Conversion serves as a conversion station for transforming analog signals into digital signals. Analog signals are susceptible to interference, are easily limited by other conditions during signal processing, and are difficult to store. Therefore, in practical … Read more

Efficient Deep Learning Computation: From TinyML to LargeLM

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Efficient Deep Learning Computation: From TinyML to LargeLM

Deep learning dominates various fields and fundamentally changes human society. Efficiency is a key factor in democratizing deep learning and expanding its application scope. This has become increasingly important as Moore’s Law slows down and the pace of model size expansion accelerates. We need efficient algorithms and systems to help bridge this gap. In this … Read more

Efficient Micro Machine Learning Systems for Edge Intelligence

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Efficient Micro Machine Learning Systems for Edge Intelligence

Source: Zhuangzhi This article introduces the thesis, recommended reading time 5 minutes. A new trend is to utilize learning paradigms on devices, bringing the end-to-end ML process closer to edge devices. Modern machine learning (ML) applications are often deployed in cloud environments to leverage the computing power of clusters. However, traditional cloud computing solutions cannot … Read more

Basic Working Principle of ADC

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Basic Working Principle of ADC

Basic Working Principle of ADC Source: CSDN Blogger:xtmtm ADC ( analog to digital converter ) conversion process The basic conversion principle of ADC is divided into four processes: ① Anti-aliasing filter ( Anti-aliasing ), which can be understood as a low-pass filter ② Sample and hold circuit ( Sample and hold ) ③ Quantization ( … Read more

Understanding ADC Types and Quantization Mechanisms

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Understanding ADC Types and Quantization Mechanisms

An Analog-to-Digital Converter (ADC) is primarily used to convert continuously transmitted analog signals into digital signals, facilitating rapid processing and analysis of transmitted information by digital systems (such as CPUs and MCUs). Generally speaking, the different quantization mechanisms of ADCs are one of the key factors determining the complexity and performance of ADC design, as … Read more

Exploring the SiRider S1 Development Board: A Comprehensive Review

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Exploring the SiRider S1 Development Board: A Comprehensive Review

This article is reprinted from the Jishu Community. Jishu Column:SiRider S1 Chip Development Board Information Author:zhangxiaolong Hello everyone, this review focuses on a domestic chip (Dragon Eagle No. 1 siengine SE1000). The main focus is on its AI capabilities, written around the theme of “how to run a complete AI application on a development board.” … Read more

Quantization and Precision Optimization of Neural Network Models in C++

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Quantization and Precision Optimization of Neural Network Models in C++

1. Introduction: The Wonderful Collision of C++ and Neural Networks In today’s technological wave, neural networks are undoubtedly a shining star, driving the field of artificial intelligence forward at an astonishing pace. From accurately identifying various objects in image recognition to enabling smooth human-computer dialogue in natural language processing, and assisting doctors in detecting disease … Read more