2 聚类量化:Deep Compression 3 线性量化:Integer-Arithmetic-Only Inference 4 训练后量化:Post-Training Quantization(PTQ) 量化粒度(Quantization Granularity) 动态范围裁剪(Dynamic Range Clipping) 舍入(Rounding) 5 量化感知训练:Quantization-Aware Training(QAT) 直通估计器:Straight-Through Estimator(STE) LSQ:在...
Model Quantization Most deep learning models are built using 32 bits floating-point precision (FP32). Quantization is the process to represent the model using less memory with minimal accuracy loss. In this context, the main focus is the representation in int8....
Model Quantization Most deep learning models are built using 32 bits floating-point precision (FP32). Quantization is the process to represent the model using less memory with minimal accuracy loss. In this context, the main focus is the representation in int8. ...
Execution-plan transformation.这个 Pass 会把一个带有复杂 sparsity pattern 的 tensor 变换为若干个简单(或者叫 regular) pattern tensor 的组合。在 SparTA 中,简单/regular pattern 被定义为只有一种 quantization bits,并且只有一种 block size 的 pruning。 为了帮助代码生成,变换后的 TeSA 会带有 bit width 和...
之前工作表明网络的裁剪是减少网络复杂度和解决过拟合问题的一种有效途径。之后的研究又发现它也是一种压缩网络的一种很有效的方式。这种方法有四个分支:量化(quantization)、二值化(binarization)、参数共享(parameter sharing)和结构化矩阵(structural matrix)。
[25] 通过训练获得最优量化区间——Quantization Interval Learning_AI Flash-CSDN博客 [26] https://github.com/NVIDIA/DeepLearningExamples/tree/master/FasterTransformer [27] https://github.com/onnx/onnx-tensorrt [28] 基于几何中位数的通道剪枝——Filter Pruning via Geometric Median_AI Flash-CSDN博客...
在边缘设备使用AI加速硬件进行模型的加速,目前主流的边缘加速硬件有:npu,tpu,gpu。 由于边缘加速硬件的计算资源有限,这里一般指其一定精度的计算核数量。笔者手中的是只支持uint8类型模型的npu, 使用google的NNAPI代理来进行网络模型的加速。 量化过程(post training quantization) ...
machine learning models. Overall, there is no one-size-fits-all solution for choosing the right tool for model quantization. Each model has strengths; understanding your specific requirements and carefully evaluating options is recommended. But for LLM, we recommend choosing Optimum Intel...
model compression based on pytorch (1、quantization: 16/8/4/2 bits(dorefa/Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference)、ternary/binary value(twn/bnn/xnor-net);2、 pruning: normal、regular and group convol
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