Practice Hardware Acceleration In Ai (3.2) - Introduction to Key Concepts: AI Algorithms, Hardware Acceleration, and Neural Network Architectures

Practice: Hardware Acceleration in AI

Lesson 3.2Introduction to Key Concepts: AI Algorithms, Hardware Acceleration, and Neural Network Architectures

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9 questions

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3 cards from this section. Quick, good the night before a test.
Exercise
6 questions · about 4 min · pass mark 70%. Hints available.
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Quiz
3 questions · about 2 min · pass mark 70%. Hints available.
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6 sample questions

Question 1.
What does hardware acceleration refer to?
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Think about how certain tools are designed for specific tasks.
Question 2.
Name one type of hardware accelerator.
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Which accelerators are commonly used for AI tasks?
3Question 3.
Which hardware accelerator is optimized for deep learning tasks?
- CPU
- GPU
- TPU
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Think about which one has 'Tensor' in its name.
4Question 4.
True or False: FPGAs are fixed-function hardware components.
- True
- False
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Consider whether they can change their functions.
Question 5.
Critically analyze the advantages and disadvantages of using GPUs versus TPUs in AI training.
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Think about the nature of the tasks each is designed for.
Question 6.
Design a simple question-and-answer format to explain how FPGAs can benefit real-time AI applications, like autonomous driving.
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Focus on the need for speed and adaptability.

5 more questions in this section. Enroll free to practise them all.