量化是一个信息有损压缩的过程,如果训练过程中使用FP32,在模型推理时使用Post-training Quantization(PTQ)直接量化为INT8模型,模型精度会存在一定损失。而量化感知训练(Quantization-aware-training, QAT)在模型训练过程中就引入了伪量化(Fake-quantization)来模拟量化过程中带来的误差,通过这种
量化的基础概念,可以通过上述资源学习。核心总结:模型量化分为Post-Training Quantization 和Quantization-Aware Training,核心思想都是通过将浮点型的权重和激活层转换定点或者整型的权重和激活层,通过公式表示:q = f / s + o ,其中q为量化后的权重值和激活层,s为伸缩值(一般为浮点,也可以通过某些技巧转换为整型或...
今天我们就讲一种既能压缩模型大小,又能加速模型推断速度:量化。 量化一般可以分为两种模式:训练后的量化(post training quantizated)和训练中引入量化(quantization aware training)。 训练后的量化理解起来比较简单,将训练后的模型中的权重由float32量化到int8,并以int8的形式保存,但是在实际推断时,还需要反量化为f...
然而，量化过程并非无损压缩，模型精度会因量化而有所损失。为减少这种损失，量化感知训练（Quantization-aware-training, QAT）引入了伪量化（Fake-quantization）策略，在训练过程中模拟量化过程中的误差，以进一步减少模型量化后的精度损失。量化感知训练在训练过程加入了模拟量化，与传统的后训练量化（Post-tr...
However, they also introduce new challenges in DL training and deployment. In this paper, we propose a novel training method that is able to compensate for quantization noise, which profoundly exists in photonic hardware due to analog-to-digital (ADC) and digital-to-analog (DAC) conversions, ...
模型量化，特别是Post-Training Quantization和Quantization-Aware Training，是将神经网络模型从浮点运算转换为定点或整数运算的重要步骤。资源获取方面，可以参考MIT的课程(<a href="hanlab.mit.edu/files/co...)和efficientml.ai/schedule...)，以及UC Berkeley的"Hardware for Machine Learning"资料(...
While extensive research has focused on weight quantization, quantization-aware training (QAT), and their application to SNNs, the precision reduction of state variables during training has been largely overlooked, potentially diminishing inference performance. This paper introduces two QAT schemes for ...
本教程介绍如何进行训练后静态量化,并演示两种更高级的技术per-channel quantization和quantization-aware training以进一步提高模型的准确性。 在本教程结束时,将看到PyTorch中的量化如何在提高速度的同时显着减小模型大小。此外,您还将了解如何轻松应用here所示的一些高级量化技术,以便量化模型的精度的损失比其他方式要小得...
I will explore topics like Asymmetric and Symmetric Quantization, Quantization Range, Quantization Granularity, Dynamic and Static Quantization, Post-Training Quantization and Quantization-Aware Training. Chapters 00:00 - Introduction 01:10 - What is quantization? 03:42 - Integer representation 07:25 -...
micronet, a model compression and deploy lib. compression: 1、quantization: quantization-aware-training(QAT), High-Bit(>2b)(DoReFa/Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference)、Low-Bit(≤2b)/Ternary and Binary(TWN/BNN/XNOR-Net); post-training-quanti...