As we design deep learning networks, how can we quickly prototype the complete algorithm—including pre- and postprocessing logic around deep neural networks…
Overview
This article discusses how to use MATLAB in conjunction with TensorRT on NVIDIA GPUs to prototype deep learning networks efficiently. It covers the process of compiling MATLAB applications into CUDA and running them on various NVIDIA platforms, emphasizing performance optimization techniques.
What You'll Learn
1
How to compile MATLAB applications into CUDA for NVIDIA GPUs
2
Why using TensorRT can improve deep learning inference performance
3
When to use cuDNN versus TensorRT for deep learning applications
4
How to implement a traffic sign detection algorithm using MATLAB
Prerequisites & Requirements
- Basic understanding of deep learning concepts and MATLAB
- MATLAB R2018b and relevant toolboxes
Key Questions Answered
How can I prototype deep learning algorithms using MATLAB on NVIDIA GPUs?
You can prototype deep learning algorithms in MATLAB by using GPU Coder to compile your MATLAB applications into CUDA, allowing them to run on NVIDIA GPUs. This process automates the translation of algorithms into CUDA, enabling faster testing and performance evaluation.
What are the steps involved in the traffic sign detection algorithm?
The traffic sign detection algorithm involves three key steps: detecting traffic signs using a YOLO-based network, applying Non-Maximal Suppression (NMS) to filter overlapping detections, and recognizing the detected signs using a classification network.
What performance gains can be expected when using TensorRT compared to cuDNN?
Using TensorRT with INT8 data types resulted in an execution time of 0.0107 seconds, achieving approximately 93 images per second, while cuDNN achieved 0.0131 seconds, or about 76 images per second. This shows a performance gain of 22% when using TensorRT.
What tools are required to run deep learning algorithms in MATLAB?
To run deep learning algorithms in MATLAB, you need MATLAB R2018b along with the Deep Learning Toolbox, Parallel Computing Toolbox, Computer Vision System Toolbox, and GPU Coder. These tools facilitate the design, training, and deployment of deep learning models.
Key Statistics & Figures
Execution time with TensorRT
0.0107 seconds
This execution time corresponds to processing approximately 93 images per second.
Execution time with cuDNN
0.0131 seconds
This execution time corresponds to processing approximately 76 images per second.
Performance gain
22%
This gain is observed when comparing TensorRT with cuDNN for single image inference.
Technologies & Tools
Software
Matlab
Used for designing and prototyping deep learning algorithms.
Software
Tensorrt
Used for optimizing deep learning models for inference on NVIDIA GPUs.
Software
Cudnn
Used as an alternative to TensorRT for deep learning model inference.
Hardware
Nvidia Gpus
The target hardware for running optimized deep learning algorithms.
Key Actionable Insights
1Utilize GPU Coder to automate the compilation of MATLAB algorithms into CUDA for improved performance on NVIDIA GPUs.This approach significantly reduces the manual effort required to translate algorithms into CUDA, allowing for quicker iterations and testing of deep learning models.
2When implementing deep learning algorithms, consider using TensorRT for enhanced inference speed and efficiency.TensorRT optimizes neural network models for inference, particularly in scenarios where low latency and high throughput are critical, such as in real-time applications.
3Leverage the MATLAB unit test framework to systematically test your deep learning models.This ensures that your models perform as expected across various scenarios, helping to identify issues early in the development process.
Common Pitfalls
1
Failing to properly configure GPU Coder can lead to suboptimal performance or compilation errors.
Ensure that the correct settings for TensorRT or cuDNN are specified in the GPU Coder configuration to maximize performance and avoid runtime issues.
2
Not testing the algorithm thoroughly in MATLAB before compiling can result in undetected bugs.
Always validate your MATLAB implementation to ensure correctness before moving to GPU execution, as debugging in CUDA can be more complex.
Related Concepts
Deep Learning Frameworks
Cuda Programming
Real-time Image Processing
Performance Optimization Techniques