Quantizzazione, proiezione e sfoltimento
Utilizzare Deep Learning Toolbox™ insieme al pacchetto di supporto Deep Learning Toolbox Model Quantization Library per ridurre l’ingombro della memoria e i requisiti di calcolo di una rete neurale profonda tramite:
Sfoltimento dei filtri dai livelli convoluzionali utilizzando l'approssimazione di Taylor del primo ordine. È quindi possibile generare codice C/C++ o CUDA® da questa rete sfoltita.
Proiezione dei livelli eseguendo l'analisi dei componenti principali (PCA) sulle attivazioni del livello utilizzando un set di dati rappresentativo dei dati di addestramento e applicando le proiezioni lineari sui parametri apprendibili del livello. I passaggi in avanti di una rete neurale profonda proiettata sono in genere più rapidi quando si distribuisce la rete su hardware integrato utilizzando la generazione di codice C/C++ senza librerie.
Quantizzazione dei pesi, dei bias e attivazioni di livelli in tipi di dati interi scalati a precisione ridotta. È quindi possibile generare codice C/C++, CUDA o HDL da questa rete quantizzata per la distribuzione su GPU, FPGA o CPU.
Per una panoramica dettagliata delle tecniche di compressione disponibili in Deep Learning Toolbox Model Quantization Library, vedere Reduce Memory Footprint of Deep Neural Networks.
Funzioni
App
| Deep Network Quantizer | Quantize deep neural network to 8-bit scaled integer data types |
Argomenti
Panoramica
- Reduce Memory Footprint of Deep Neural Networks
Learn about neural network compression techniques, including pruning, projection, and quantization.
Sfoltimento
- Analyze and Compress 1-D Convolutional Neural Network
Analyze 1-D convolutional network for compression and compress it using Taylor pruning and projection. (Da R2024b) - Parameter Pruning and Quantization of Image Classification Network
Use parameter pruning and quantization to reduce network size. - Prune Image Classification Network Using Taylor Scores
This example shows how to reduce the size of a deep neural network using Taylor pruning. - Prune Filters in a Detection Network Using Taylor Scores
This example shows how to reduce network size and increase inference speed by pruning convolutional filters in a you only look once (YOLO) v3 object detection network. - Prune and Quantize Convolutional Neural Network for Speech Recognition
Compress a convolutional neural network (CNN) to prepare it for deployment on an embedded system.
Proiezione e distillazione della conoscenza
- Compress Neural Network Using Projection
This example shows how to compress a neural network using projection and principal component analysis. - Evaluate Code Generation Inference Time of Compressed Deep Neural Network
This example shows how to compare the inference time of a compressed deep neural network for battery state of charge estimation. (Da R2023b) - Train Smaller Neural Network Using Knowledge Distillation
This example shows how to reduce the memory footprint of a deep learning network by using knowledge distillation. (Da R2023b)
Quantizzazione
- Quantization of Deep Neural Networks
Overview of the deep learning quantization tools and workflows. - Quantization Workflow Prerequisites
Products required for the quantization of deep learning networks. - Prepare Data for Quantizing Networks
Supported datastores for quantization workflows. - Quantize Multiple-Input Network Using Image and Feature Data
Quantize Multiple Input Network Using Image and Feature Data - Export Quantized Networks to Simulink and Generate Code
Export a quantized neural network to Simulink and generate code from the exported model.
Quantizzazione per il target GPU
- Generate INT8 Code for Deep Learning Networks (GPU Coder)
Quantize and generate code for a pretrained convolutional neural network. - Quantize Residual Network Trained for Image Classification and Generate CUDA Code
This example shows how to 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. - Quantize Layers in Object Detectors and Generate CUDA Code
This example shows how to 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 Semantic Segmentation Network and Generate CUDA Code
Quantize Convolutional Neural Network Trained for Semantic Segmentation and Generate CUDA Code
Quantizzazione per il target FPGA
- Quantize Network for FPGA Deployment (Deep Learning HDL Toolbox)
Reduce the memory footprint of a deep neural network by quantizing the weights, biases, and activations of convolution layers to 8-bit scaled integer data types. - Classify Images on FPGA Using Quantized Neural Network (Deep Learning HDL Toolbox)
This example shows how to use Deep Learning HDL Toolbox™ to deploy a quantized deep convolutional neural network (CNN) to an FPGA. - 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.
Quantizzazione per il target CPU
- Generate int8 Code for Deep Learning Networks (MATLAB Coder)
Quantize and generate code for a pretrained convolutional neural network. - Generate INT8 Code for Deep Learning Network on Raspberry Pi (MATLAB Coder)
Generate code for deep learning network that performs inference computations in 8-bit integers.
Esempi in primo piano
Prune Image Classification Network Using Taylor Scores
Reduce the size of a deep neural network using Taylor pruning. By using the taylorPrunableNetwork function to remove convolution layer filters, you can reduce the overall network size and increase the inference speed.
Prune Filters in a Detection Network Using Taylor Scores
Reduce network size and increase inference speed by pruning convolutional filters in a you only look once (YOLO) v3 object detection network.
Compress Neural Network Using Projection
Compress a neural network using projection and principal component analysis.
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.
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.
