剪枝(Pruning) 剪枝是一种通过移除模型中不重要或不活跃的参数(通常是权重)来减少模型大小的技术。它通过分析哪些参数对最终输出影响较小,然后将这些参数从模型中删除,从而减少计算量和内存使用。 剪枝后,模型的参数数量减少,但通常仍然保持类似的性能。 举例 有一棵树(这棵树代表你的模型),这棵树上有许多枝条(这些枝条代表模型中的参数)。
Tensorflow:https://www.tensorflow.org/lite/performance/post_training_quantization Pytorch:https://pytorch.org/docs/master/quantization.html 如果想了解量化具体实现,可参考这篇论文还有它的实现,其中不光包括量化还有后面提到的剪枝: 论文:transformers.zip: Compressing Transformers with Pruning and Quantization 代...
The pruning method in this paper sets a parameter, that can be adjusted to meet different pruning rates in practical applications. The quantization method converts high-precision weights to low-precision weights. The latter are all composed of 0 and powers of 2. In the same way, another ...
Deep Neural Network Compression by In-Parallel Pruning-Quantization 论文笔记 摘要 深度神经网络在视觉识别任务(如图像分类和物体检测)上实现了最先进的精确度。然而,现代网络包含数百万个已学习的连接,并且当前的趋势是朝向更深和更密集连接的体系结构。这对在资源受限的系统(例如智能手机或移动机器人)上部署最先进...
模型加速--CLIP-Q: Deep Network Compression Learning by In-Parallel Pruning-Quantization,程序员大本营,技术文章内容聚合第一站。
We discuss trade-offs in element-wise, channel-wise, shape-wise, filter-wise, layer-wise and even network-wise pruning. Quantization reduces computations by reducing the precision of the datatype. Weights, biases, and activations may be quantized typically to 8-bit integers although lower bit ...
Compress a deep neural network by performing quantization or pruningUse Deep Learning Toolbox™ together with the Deep Learning Toolbox Model Quantization Library support package to reduce the memory footprint and computational requirements of a deep neural network by: Quantizing the weights, biases...
模型的部署分为 模型结构设计(Architecture),剪枝(Pruning),量化(Quantization)三个步骤。 这三步的pipeline可以显著减少模型开销,但当同时考虑这三个优化目标时,待优化的参数/时间开销会急剧增加;且三个步骤单独搜索可能会导致最终的结果陷入局部最优(如,全精度下最佳的结构,可能并不适合量化)。
pruning, also known as sparsification, is a compression technique that aims to identify redundant, unnecessary connections you can remove without affecting the network accuracy. When you use pruning in combination with network quantization, you can reduce the inference speed and memory ...
deep compression:compressing deep neural networks with pruning,trained quantization and huffman codi,程序员大本营,技术文章内容聚合第一站。