The Essentials of Computer Organization and Architecture
4th Edition
ISBN: 9781284045611
Author: Linda Null, Julia Lobur
Publisher: Jones & Bartlett Learning
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Chapter 11, Problem 13E
Explanation of Solution
Reaction in buying the system:
- The main problem with the benchmarks is that they are very simple that the manufactures are able to optimize their products in a way it is capable to meet their requirements based on the benchmarks derived.
- Thus, it would be very difficult for its user to obtain a decision because product will be available in the market without any differentiation.
- In order to make the benchmarks more meaningful and comprehensive, the consortium of computer manufactures created a magazine where the technology are being brought together in the form of SPEC(Standard Performance Evaluation Corporation) in 1988.
- Performance will be major criteria for the end user of the CPU’s and the performance measures are being benefited through the benchmarks that are constructed by the SPEC.
- Another class of end user whose major area of concern could be the software that are widely applied in server that are used in processing multiple transactions at a particular amount of time by different users.
- The users are very much interested with the system that is capable of processing short tasks simultaneously.
- Standardizing the analysis of the performance measure of advanced system troubled the manufacturers greatly.
- The results that are produced were being flashed at instant among the potential customers but the output that is provided is not meant for the public user...
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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…
Chapter 11 Solutions
The Essentials of Computer Organization and Architecture
Ch. 11 - Prob. 1RETCCh. 11 - Prob. 2RETCCh. 11 - Prob. 3RETCCh. 11 - Prob. 4RETCCh. 11 - Prob. 5RETCCh. 11 - Prob. 6RETCCh. 11 - Prob. 7RETCCh. 11 - Prob. 8RETCCh. 11 - Prob. 9RETCCh. 11 - Prob. 10RETC
Ch. 11 - Prob. 11RETCCh. 11 - Prob. 12RETCCh. 11 - Prob. 13RETCCh. 11 - Prob. 14RETCCh. 11 - Prob. 15RETCCh. 11 - Prob. 16RETCCh. 11 - Prob. 17RETCCh. 11 - Prob. 18RETCCh. 11 - Prob. 19RETCCh. 11 - Prob. 20RETCCh. 11 - Prob. 1ECh. 11 - Prob. 2ECh. 11 - Prob. 3ECh. 11 - Prob. 4ECh. 11 - Prob. 5ECh. 11 - Prob. 6ECh. 11 - Prob. 7ECh. 11 - Prob. 8ECh. 11 - Prob. 10ECh. 11 - Prob. 11ECh. 11 - Prob. 12ECh. 11 - Prob. 13ECh. 11 - Prob. 14ECh. 11 - Prob. 15ECh. 11 - Prob. 16ECh. 11 - Prob. 17ECh. 11 - Prob. 18ECh. 11 - Prob. 19ECh. 11 - Prob. 20ECh. 11 - Prob. 21ECh. 11 - Prob. 22ECh. 11 - Prob. 23ECh. 11 - Prob. 24ECh. 11 - Prob. 25ECh. 11 - Prob. 26E
Knowledge Booster
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