NCA-GENM Question 154
Select 4You are working on a generative AI multimodal model that synthesizes text and image data. The model is computationally expensive and suffers from decreased performance during inference on large datasets. Which approaches can you implement to enhance computational efficiency while improving accuracy?
- A
Implement mixed precision training to reduce memory usage and accelerate computations.
- B
Use knowledge distillation to transfer knowledge from a larger model to a smaller, more efficient model.
- C
Increase the batch size significantly to process more data at once, regardless of memory constraints.
- D
Prune unnecessary parameters in the model to reduce its size and maintain critical performance metrics.
- E
Replace traditional dense layers with sparse layers to reduce computation while preserving important features.
Show answer and explanation
Correct answers: A, B, D, E
Explanation
To enhance computational efficiency and improve accuracy in AI models, techniques like mixed precision training, knowledge distillation, pruning, and sparse layers are highly effective. These methods optimize resource usage and model structure while ensuring the output quality remains high. Increasing the batch size without considering memory limitations, however, is not a viable approach as it can lead to inefficiencies and errors.
- A. Correct.
Mixed precision training decreases memory usage by combining 16-bit and 32-bit floating-point precision, leading to faster computations without sacrificing accuracy.
- B. Correct.
Knowledge distillation transfers the performance of a large 'teacher' model to a smaller 'student' model, improving efficiency and maintaining accuracy.
- C. Incorrect.
Increasing the batch size significantly without considering memory constraints can lead to out-of-memory errors and is not a recommended approach for enhancing computational efficiency.
- D. Correct.
Pruning removes unnecessary weights in the model, which reduces its size while retaining critical parameters, improving efficiency and maintaining accuracy.
- E. Correct.
Sparse layers use fewer connections compared to dense layers, reducing computational overhead while preserving essential features needed for accuracy.