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 11, Problem 2E

Explanation of Solution

Verification of the ratio being consistent when compared with the other system:

  • System performance is considered as one of main factor of a processor.
  • It is used to determine the speed a problem can be solved.
  • It is also used to determine the factors such as number of problems that can be allocated at particular amount of time and also the number of problems that can be handled by the processor.
  • The relative performance between the two systems is measured and expected by the run time of the program of the individual.

Given:

The information about the system A and System c are shown in the below table:

Program

Execution time

System A(sec)

Execution time

System B(sec)

Execution time

System C(sec)

V50100500
W200400600
X250500500
Y400800800
Z500041003500

Consider there are n programs and each programs are considered to have their own runtime on each systems.

The geometric mean of the one system’s runtime is obtained by normalizing it with the another system.

The process of normalization is carried out by taking the products of the ratio of the run time and by taking the nth root of the product.

The below tables illustrates the how the system B and system C is being normalized with that of the system A.

Program

Execution time

System A(sec)

Normalized to A

Execution time

System B(sec)

Normalized to B

Execution time

System C(sec)

Normalized to C
V5011000.55000.1
W20014000.56000.33
X25015000.55000.5
Y40018000.68000.5
Z5000141001.2235001.43

Calculating geometic Mean:

The formula to calculate the geometric mean:

G=(x1×x2×x3×...×xn)1n

System A:

calculate the geometric mean for the system A:

G=(1×1×1×1×1)15=1

System B:

calculate the geometric mean for the system B:

G=(0

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Here is a clear background and explanation of the full method, including what each part is doing and why. Background & Motivation Missing values: Some input features (sensor channels) are missing for some samples due to sensor failure or corruption. Missing labels: Not all samples have a ground-truth RUL value. For example, data collected during normal operation is often unlabeled. Most traditional deep learning models require complete data and full labels. But in our case, both are incomplete. If we try to train a model directly, it will either fail to learn properly or discard valuable data. What We Are Doing: Overview We solve this using a Teacher–Student knowledge distillation framework: We train a Teacher model on a clean and complete dataset where both inputs and labels are available. We then use that Teacher to teach two separate Student models:  Student A learns from incomplete input (some sensor values missing). Student B learns from incomplete labels (RUL labels missing…
here is a diagram code : graph LR subgraph Inputs [Inputs] A[Input C (Complete Data)] --> TeacherModel B[Input M (Missing Data)] --> StudentA A --> StudentB end subgraph TeacherModel [Teacher Model (Pretrained)] C[Transformer Encoder T] --> D{Teacher Prediction y_t} C --> E[Internal Features f_t] end subgraph StudentA [Student Model A (Trainable - Handles Missing Input)] F[Transformer Encoder S_A] --> G{Student A Prediction y_s^A} B --> F end subgraph StudentB [Student Model B (Trainable - Handles Missing Labels)] H[Transformer Encoder S_B] --> I{Student B Prediction y_s^B} A --> H end subgraph GroundTruth [Ground Truth RUL (Partial Labels)] J[RUL Labels] end subgraph KnowledgeDistillationA [Knowledge Distillation Block for Student A] K[Prediction Distillation Loss (y_s^A vs y_t)] L[Feature Alignment Loss (f_s^A vs f_t)] D -- Prediction Guidance --> K E -- Feature Guidance --> L G --> K F --> L J -- Supervised Guidance (if available) --> G K…
details explanation and background   We solve this using a Teacher–Student knowledge distillation framework: We train a Teacher model on a clean and complete dataset where both inputs and labels are available. We then use that Teacher to teach two separate Student models:  Student A learns from incomplete input (some sensor values missing). Student B learns from incomplete labels (RUL labels missing for some samples). We use knowledge distillation to guide both students, even when labels are missing. Why We Use Two Students Student A handles Missing Input Features: It receives input with some features masked out. Since it cannot see the full input, we help it by transferring internal features (feature distillation) and predictions from the teacher. Student B handles Missing RUL Labels: It receives full input but does not always have a ground-truth RUL label. We guide it using the predictions of the teacher model (prediction distillation). Using two students allows each to specialize in…
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