양자화
계층의 가중치, 편향 및 활성화를 정수 데이터형으로 스케일링한 낮은 정밀도로 양자화합니다. 그런 다음 양자화된 이러한 신경망에서 GPU, FPGA 또는 CPU 배포용 C/C++, CUDA® 또는 HDL 코드를 생성할 수 있습니다.
Deep Learning Toolbox™ Model Compression Library에서 사용 가능한 압축 기법에 대한 자세한 개요는 Reduce Memory Footprint of Deep Neural Networks 항목을 참조하십시오.
함수
dlquantizer | Quantize a deep neural network to 8-bit scaled integer data types |
dlquantizationOptions | Options for quantizing a trained deep neural network |
prepareNetwork | Prepare deep neural network for quantization (R2024b 이후) |
calibrate | Simulate and collect ranges of a deep neural network |
quantize | Quantize deep neural network (R2022a 이후) |
validate | Quantize and validate a deep neural network |
quantizationDetails | 신경망의 양자화 세부 정보 표시 (R2022a 이후) |
estimateNetworkMetrics | Estimate network metrics for specific layers of a neural network (R2022a 이후) |
equalizeLayers | Equalize layer parameters of deep neural network (R2022b 이후) |
exportNetworkToSimulink | Generate Simulink model that contains deep learning layer blocks and subsystems that correspond to deep learning layer objects (R2024b 이후) |
앱
| 심층 신경망 양자화기 | Quantize deep neural network to 8-bit scaled integer data types |
도움말 항목
양자화 이해하기
- Quantization of Deep Neural Networks
Learn about deep learning quantization tools and workflows. - Data Types and Scaling for Quantization of Deep Neural Networks
Understand effects of quantization and how to visualize dynamic ranges of network convolution layers.
배포 전 워크플로
- Prepare Data for Quantizing Networks
Learn about supported data formats for quantization workflows. - Quantize Multiple-Input Network Using Image and Feature Data
Quantize a network with multiple inputs. - Export Quantized Networks to Simulink and Generate Code
Export a quantized neural network to Simulink and generate code from the exported model. - Quantization-Aware Training with Pseudo-Quantization Noise
Perform quantization-aware training with pseudo-quantization noise on the MobileNet-V2 network. (R2026a 이후)
배포
- Quantize Semantic Segmentation Network and Generate CUDA Code
Quantize a convolutional neural network trained for semantic segmentation and generate CUDA code. - Classify Images on FPGA by Using Quantized GoogLeNet Network (Deep Learning HDL Toolbox)
This example shows how to use the Deep Learning HDL Toolbox™ to deploy a quantized GoogleNet network to classify an image. - Compress Image Classification Network for Deployment to Resource-Constrained Embedded Devices
Reduce the memory footprint and computation requirements of an image classification network for deployment to resource-constrained embedded devices such as the Raspberry Pi®.
고려 사항
- Quantization Workflow System Requirements
See what products are required for the quantization of deep neural networks. - Supported Layers for Quantization
Learn which deep neural network layers are supported for quantization.
추천 예제
Quantize Deep Learning Network for Battery State of Charge Estimation
Quantize recurrent neural network trained for battery state of charge estimation.
Prune and Quantize Convolutional Neural Network for Speech Recognition
Compress a convolutional neural network (CNN) to prepare it for deployment on an embedded system.
Parameter Pruning and Quantization of Image Classification Network
Use parameter pruning and quantization to reduce network size.
Prune and Quantize Semantic Segmentation Network
Reduce the memory footprint of a semantic segmentation network and speed-up inference by compressing the network using pruning and quantization.
Quantize Layers in Object Detectors and Generate CUDA Code
Generate CUDA® code for an SSD vehicle detector and a YOLO v2 vehicle detector that performs inference computations in 8-bit integers for the convolutional layers.
Quantize Residual Network Trained for Image Classification and Generate CUDA Code
Quantize the learnable parameters in the convolution layers of a deep learning neural network that has residual connections and has been trained for image classification with CIFAR-10 data.
