CIS 4930/CIS 6930 Hardware Accelerators for Machine Learning Lecture 07 Quiz Review
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Questions and Answers

What is the purpose of today's lecture?

  • To review hardware architecture
  • To introduce a new topic in machine learning
  • To discuss performance and benchmarking
  • To prepare for the upcoming quiz (correct)
  • Which items should students focus on to study for the quiz?

  • Lecture 6 – Performance and Benchmarking
  • Lecture 5 – ML with Google Colab
  • Lecture 4 – Hardware architecture WE
  • Lecture 3 – ML Basics (CNN) (correct)
  • What is the main topic discussed in Part A of the recap?

  • Hardware architecture
  • Performance and Benchmarking
  • ML Basics (CNN) (correct)
  • ML with Google Colab
  • What does SISD stand for in the context of computer architecture?

    <p>Single instruction operates on single data element</p> Signup and view all the answers

    Which architecture is characterized by multiple instructions operating on single data elements?

    <p>MISD</p> Signup and view all the answers

    In the context of GPU vs. CPU, which characteristic is associated with GPU?

    <p>More generalization</p> Signup and view all the answers

    What does the Perceptron model primarily aim to understand?

    <p>Bias and synapse</p> Signup and view all the answers

    What is the purpose of local receptive fields in a CNN?

    <p>To make connections in small, localized regions of the input image</p> Signup and view all the answers

    What is the benefit of sharing weights and biases in a CNN?

    <p>Greatly reduced number of parameters</p> Signup and view all the answers

    Which layer typically follows convolution layers in a CNN?

    <p>Pooling layer</p> Signup and view all the answers

    What is the function of max-pooling in a CNN?

    <p>Outputs the maximum value of the region's neurons</p> Signup and view all the answers

    What does CNN stand for in the context of this text?

    <p>Convolutional Neural Network</p> Signup and view all the answers

    What role does shared weights and biases play in CNN?

    <p>Reduces the computational complexity and number of parameters</p> Signup and view all the answers

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