Practice Algorithmic Optimization (8.4.1) - Optimization of AI Circuits

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8 questions

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6 sample questions

Question 1.
Define model pruning.
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Think about what happens when you reduce the complexity of a network.
Question 2.
What is quantization?
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Consider the difference between 32-bit and 8-bit data types.
3Question 3.
What is the main goal of algorithmic optimization?
- Decrease accuracy
- Reduce computational requirements
- Increase memory size
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Think about efficiency in computations.
4Question 4.
True or False: Model pruning increases the size of a neural network.
- True
- False
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Consider what pruning means.
Question 5.
Given a neural network with 1M weights, if applying model pruning reduces weights to 200K while maintaining 90% accuracy, discuss possible implications for deployment in edge devices.
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Consider factors like processing time and memory in edge applications.
Question 6.
Analyze the trade-offs involved with quantization in a deep learning model that previously used 32-bit floats. What are the possible impacts on performance and accuracy?
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Reflect on the effects of reduced precision on output.

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