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 16E
Explanation of Solution
Storage required for gathering the data:
- 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 speed of the CPU is a key factor that often decides the performance of the system.
- But this factor speed is applied only to identical architecture and which will be misled at many cases.
- A detailed analysis is made to arrive at a detailed decision for different architecture.
- Ensuring reliability with the users in terms performance certain set of standards are established.
- Benchmarks are being established such that user can compare the metrics to get a clear idea about the performance.
- The main problem with the benchmarks that are obtained is that are very simple in such a way 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 difficult for its user to obtain a decision because product will be available in the market without any differentiation.
- Thus to make the benchmarks being 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.
- Obtaining a practical means to measure the computer performance is considered to be the main objective of the group.
- The benchmarks were set up based on the empirical data that are gather over a significant period of time.
Given:
- The processor that is under the observation has the clock running time =1GHZ which is equivalent to (109) ticks per second...
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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
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