Large models are sweeping the automobile industry. Is this a real trend or a false demand?
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The article mentioned that in the automotive industry, the application of large models faces some technical difficulties:
Model compression and deployment : Due to the limitations of the vehicle environment, large models need to be compressed to a size suitable for the embedded chip in the vehicle while maintaining the performance of the model. This requires solving challenges in model compression algorithms, hardware acceleration, etc.
Real-time and low-latency : The automotive industry has strict real-time and low-latency requirements for applications such as smart driving and voice assistants. Large models need to perform reasoning and decision-making within a limited time and respond to user instructions in a timely manner.
Generalization ability of the model : Large models need to have strong generalization ability in different scenarios and environments, and can adapt to various complex driving situations and user needs. At present, no large model of autonomous driving has yet achieved such capabilities.
Three technical problems faced by large models in the automotive industry
Model compression and deployment
Real-time and low latency
The generalization ability of the model
How can we study feasible solutions?
1/ Model compression and deployment:
Research existing model compression algorithms and techniques to understand their effectiveness in reducing model parameters and computational effort.
Explore hardware acceleration techniques, such as using specialized chips or neural network accelerators, to increase the speed and efficiency of model inference.
Experiment with different model structures and architectures to find the optimal model size for embedded chips in the car.
2/ Real-time and low latency:
Research high-performance computing and parallel computing technologies to improve the speed and response time of model inference.
Explore the application of distributed computing and edge computing to move some computing tasks from the cloud to the vehicle itself to reduce latency.
Optimize the model's inference process, such as using techniques such as quantization and pruning, to reduce the amount of calculation and accelerate the inference process.
3/ Generalization ability of the model:
Collect diverse scene data and build rich data sets to train the model's generalization ability in various complex driving situations.
Research transfer learning and domain adaptation techniques to improve model performance in different environments and scenarios.
Explore methods of incremental learning and online learning to enable models to continuously learn and adapt from new data.
shadow: The problems encountered when applying it in other industries are similar:
Model compression and deployment
Real-time and low latency
The generalization ability of the model
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