Methods and apparatuses of neural network model compression/decompression are described. In some examples, an apparatus of neural network model decompression includes receiving circuitry and processing circuitry. The processing circuitry can be configured to receive a dependent quantization enabling flag from...
DMC 2020-CVPR-Discrete Model Compression with Resource Constraint for Deep Neural Networks 来源:ChenBong 博客园 Institute:University of Pittsburgh、Simon Fr
【模型压缩和加速综述】A Survey of Model Compression and Acceleration for Deep Neural Networks 目录 摘要 1. 引言 2. 参数剪枝和量化 回到顶部 摘要 深度神经网络(DNNs)最近在许多视觉识别任务中取得了巨大的成功。然而,现有的深度神经网络模型计算成本高,内存 密集(intensive),阻碍(hindering)了它们在内存资源低...
早期剪枝的方法是偏差权重衰减(Biased Weight Decay),其中最优脑损伤(Optimal Brain Damage)和最优脑手术(Optimal Brain Surgeon)方法,是基于损失函数的Hessian矩阵来减少连接的数量,实验表明这种剪枝的方法比通过权值绝对值阈值的剪值更有效。 最近的工作倾向是去裁剪预训练模型(pre-trained CNN model)冗余或者没有信息...
A Comprehensive Survey on Model Quantization for Deep Neural Networks in Image ClassificationQuantizationmodel compressiondeep neural network accelerationimage ... B Rokh,A Azarpeyvand,A Khanteymoori - 《Acm Transactions on Intelligent Systems & Technology》 被引量: 0发表: 2023年 Advanced Image Compre...
文献[37]中的工作提出使用不同的张量分解方案,报告4.5倍加速,文本识别精度下降1%。低阶逼近是逐层完成的。完成一层的参数后,根据重建误差准则对上述层进行微调。这些是压缩二维卷积层的典型低秩方法,如图2所示。遵循这个方向,对[38]中的核张量提出了Canonical Polyadic(CP)分解。他们的工作使用非线性最小二乘法来...
Neural Networks (CNNs). Firstly, a pre-trained CNN model is pruned layer by layer according to the sensitivity of each layer. After that, the pruned model is fine-tuned based on knowledge distillation framework. These two improvements significantly decrease the model redundancy with less accuracy...
deep neural networks; model compression; model pruning; parameter quantization; low-rank decomposition; knowledge distillation; lightweight model design1. Introduction In recent years, due to the rapid development of artificial intelligence, machine learning has received a great deal of attention from ...
Model Compression in the Era of Large Language Models Guest editors: Xianglong Liu; Michele Magno; Haotong Qin; Ruihao Gong; Tianlong Chen; Beidi Chen Large language models (LLMs), as series of large-scale, pre-trained, statistical language models based on neural networks, have achieved signif...
Model Compression in the Era of Large Language Models Guest editors: Xianglong Liu; Michele Magno; Haotong Qin; Ruihao Gong; Tianlong Chen; Beidi Chen Large language models (LLMs), as series of large-scale, pre-trained, statistical language models based on neural networks, have achieved signif...