NCA-GENM Question 158
Select 3You are working on a multimodal generative AI model that processes both text and images to generate creative content. The model is computationally intensive and struggles with latency during inference. Which of the following techniques can enhance computational efficiency while maintaining or improving the accuracy of the outputs?
- A
Use mixed precision training to reduce memory usage and speed up computations.
- B
Implement model pruning to remove less significant parameters from the model.
- C
Increase the number of layers in the model to improve its capacity.
- D
Leverage knowledge distillation to train a smaller, faster model using a larger, pre-trained model as a teacher.
- E
Use larger batch sizes during inference to optimize GPU utilization.
Show answer and explanation
Correct answers: A, B, D
Explanation
Enhancing computational efficiency while preserving accuracy often involves optimized training and inference strategies. Mixed precision training, model pruning, and knowledge distillation are well-established techniques that reduce computational requirements without compromising model performance. Increasing model complexity or using larger batch sizes during inference does not align with the goal of improving efficiency.
- A. Correct.
Mixed precision training uses lower precision (e.g., FP16) for certain computations, reducing memory usage and improving speed without significantly affecting accuracy. This is a widely used technique for enhancing computational efficiency.
- B. Correct.
Model pruning removes unnecessary parameters from the model, reducing its size and computational requirements while maintaining accuracy, making it a suitable optimization method.
- C. Incorrect.
Increasing the number of layers in the model would typically make it more complex and computationally expensive, which contradicts the goal of improving efficiency.
- D. Correct.
Knowledge distillation creates a smaller, more efficient model by transferring knowledge from a larger model, which can maintain high accuracy while improving computational efficiency.
- E. Incorrect.
While larger batch sizes can improve GPU utilization during training, they are not typically used during inference and do not directly address computational efficiency or accuracy concerns.