9.2.2. Integration of AI Algorithms with Hardware

Try these first

1. 1.

   What is quantization in neural networks?

   Hint

   Think about how precision impacts memory usage.

2. 2.

   Define pruning in the context of AI models.

   Hint

   Consider how this could simplify a model.

3. 3.

   What technique reduces the precision of neural network weights to improve efficiency?

   • Pruning
   • Quantization
   • Normalization

   Hint

   Think about how making weights less precise affects memory.

4. 4.

   True or False: Pruning increases the size and complexity of the model.

   • True
   • False

   Hint

   What is the purpose of pruning in a model?

5. 5.

   Consider a neural network that is pruned excessively. Describe the potential outcomes for the model’s performance and accuracy.

   Hint

   What happens to any model when you simplify it too much?

6. 6.

   You are tasked with deploying an AI model on an edge device with limited resources. Discuss how you would apply quantization and pruning to maximize efficiency.

   Hint

   Focus on the implications of resource constraints in AI deployment.