○ TensorRT is an SDK for high-performance deep learning inference, and TensorRT 8.0 introduces support for sparsity that uses sparse tensor cores on NVIDIA…
Overview
This article discusses how the NVIDIA Ampere Architecture and TensorRT 8.0 leverage sparsity to accelerate neural network inference. It highlights the benefits of 2:4 fine-grained structured sparsity, which allows for significant performance improvements without sacrificing accuracy.
What You'll Learn
1
How to implement 2:4 structured sparsity in neural networks
2
Why using Sparse Tensor Cores can improve inference performance
3
How to use TensorRT 8.0 for deploying sparse models
Prerequisites & Requirements
- Understanding of neural network architectures and training processes
- Familiarity with NVIDIA TensorRT and PyTorch(optional)
Key Questions Answered
How does the NVIDIA Ampere Architecture improve neural network inference?
The NVIDIA Ampere Architecture enhances neural network inference by introducing Sparse Tensor Cores that support 2:4 fine-grained structured sparsity. This allows for significant performance improvements, achieving over 30% performance/watt gain compared to dense networks while maintaining accuracy.
What is the workflow for creating a 2:4 structured sparse network?
The workflow involves starting with a dense network, pruning weights to meet the 2:4 sparsity criteria, and then retraining the model to recover accuracy. This process ensures that the sparse model retains the performance of the original dense model.
What performance improvements can be expected from using TensorRT 8.0?
Using TensorRT 8.0 with sparse models on an A100 GPU can lead to performance improvements that increase with batch size, achieving up to 20% better performance and up to 36% performance/watt gain, all while maintaining the same accuracy as the dense baseline.
Key Statistics & Figures
Performance/watt gain
over 30%
This gain is achieved when using Sparse Tensor Cores compared to dense networks.
Accuracy retention
76.2% for ResNet-50 sparse model
This accuracy is comparable to the dense model's accuracy of 76.1%, demonstrating the effectiveness of the pruning workflow.
Technologies & Tools
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Inference Optimization
Nvidia Tensorrt
Used for high-performance deep learning inference and to deploy sparse models efficiently.
Deep Learning Framework
Pytorch
Utilized for training and implementing the sparse models.
Key Actionable Insights
1Implementing 2:4 structured sparsity can significantly enhance the efficiency of neural networks.By adopting this sparsity technique, developers can reduce computational overhead and improve inference speed without sacrificing model accuracy, making it suitable for deployment in resource-constrained environments.
2Utilizing TensorRT 8.0 is crucial for maximizing the performance of sparse models.TensorRT 8.0 is designed to optimize inference for deep learning models, and leveraging its capabilities can lead to substantial performance gains in production scenarios.
Common Pitfalls
1
Failing to maintain accuracy after pruning can lead to ineffective models.
Without a proper retraining process after pruning, the model's performance may degrade. It's essential to follow a structured workflow to recover accuracy.
Related Concepts
Neural Network Pruning
Model Compression Techniques
Deep Learning Inference Optimization