Essentials of Computer Organization and Architecture
Essentials of Computer Organization and Architecture
4th Edition
ISBN: 9781284074482
Author: Linda Null, Julia Lobur
Publisher: Jones & Bartlett Learning
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Chapter 5, Problem 23E

Explanation of Solution

Explanation:

The time taken by the non-pipelined system to process the task is 200ns

The time taken by the five-segment pipeline to process the task using clock cycles is 40ns

The number of tasks present in the pipeline is 200

Using five-segment pipeline:

Assume a k-stage pipeline which includes clock cycle time of tp with n number of tasks to process.

The time required by the task 1 to complete the process is k×tp

The time required to process the remaining tasks is (n1)tp

The time required to complete n tasks using a k-stage pipeline can be calculated as follows

=k×tp+(n1)×tp=(k+n1)×tp

Thus, the time taken to complete n tasks using k-stage pipeline is =(k+n1)×tp

Without using pipeline:

The time required by the task 1 to complete the process without using pipeline is tn=k×tp

The number of cycles required for n tasks is n×tn

Speedup:

The speedup gained can be calculated using the following formula

Ac

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Please provide me with the output  image of both of them . below are the diagrams code  make sure to update the code and mentionned clearly each section also the digram should be clearly describe like in the attached image. please do not provide the same answer like in other question . I repost this question because it does not satisfy the requirment I need in terms of clarifty the output of both code are not very well details  I have two diagram : first diagram code  graph LR subgraph Teacher Model (Pretrained) Input_Teacher[Input C (Complete Data)] --> Teacher_Encoder[Transformer Encoder T] Teacher_Encoder --> Teacher_Prediction[Teacher Prediction y_T] Teacher_Encoder --> Teacher_Features[Internal Features F_T] end subgraph Student_A_Model[Student Model A (Handles Missing Values)] Input_Student_A[Input M (Data with Missing Values)] --> Student_A_Encoder[Transformer Encoder E_A] Student_A_Encoder --> Student_A_Prediction[Student A Prediction y_A] Student_A_Encoder…
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Here are two diagrams. Make them very explicit, similar to Example Diagram 3 (the Architecture of MSCTNN). graph LR subgraph Teacher_Model_B [Teacher Model (Pretrained)] Input_Teacher_B[Input C (Complete Data)] --> Teacher_Encoder_B[Transformer Encoder T] Teacher_Encoder_B --> Teacher_Prediction_B[Teacher Prediction y_T] Teacher_Encoder_B --> Teacher_Features_B[Internal Features F_T] end subgraph Student_B_Model [Student Model B (Handles Missing Labels)] Input_Student_B[Input C (Complete Data)] --> Student_B_Encoder[Transformer Encoder E_B] Student_B_Encoder --> Student_B_Prediction[Student B Prediction y_B] end subgraph Knowledge_Distillation_B [Knowledge Distillation (Student B)] Teacher_Prediction_B -- Logits Distillation Loss (L_logits_B) --> Total_Loss_B Teacher_Features_B -- Feature Alignment Loss (L_feature_B) --> Total_Loss_B Partial_Labels_B[Partial Labels y_p] -- Prediction Loss (L_pred_B) --> Total_Loss_B Total_Loss_B -- Backpropagation -->…
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