Machine learning based thermal analysis of on-chip and chiplet-based systems
Notice bibliographique
Résumé
Following Moore's Law, the number of transistors on chips has continued to increase, Dennard Scaling however has not kept pace.The increase in power density caused by this phenomena has lead to increased temperatures which must be addressed, as high temperatures lead to unfavorable effects on system performance and reliability.A key challenge in the design process is therefore to identify problematic designs early, to avoid significant computation and time spent on designs which are thermally unviable.However, traditional thermal analysis tools such as finite element method (FEM) solvers or compact thermal models (CTM), are computationally costly and time-consuming thus proving unsuitable for iterative processes, such as those at early stages in the chip design process.Researchers, therefore, have been striving to develop fast and accurate methods of predicting the chip temperature.Recently machine learning (ML) based solutions have shown great promise in a variety of electronic design automation (EDA) applications, including that of design space reduction and exploration.Neural networks (NN) have proven to be especially effective, due to their ability to accurately and efficiently learn and Abstract ii embed the relationships between design parameters and performance metrics.When applied for thermal analysis tasks these models are able to predict at much faster rates then FEMs and CTMs making them more suitable for iterative processes, such as those found at early stages in the chip design process.For the thermal analysis task, specific types of NNs are used, typically either convolutional neural networks (CNN) or graph neural network (GNN) based architectures.These types of models are ideal due to their ability to learn based off the locality of elements, a parameter that is especially important in thermal phenomena.To accurately predict the temperature, proper data structures are required.Common implementations utilize data from early stages, such as functional block placements and power density maps to predict hot spots or thermal maps.This lack of sophisticated data structures coupled with the absence of training datasets that reflect realistic system-on-chip and chiplet-based designs limits the applicability and generalizability of these models.In this thesis, the effects of thermal properties and thermal design power on a systems overall performance are discussed.Then, there is a more focused discussion on thermal analysis of on-chip and chiplet-based systems and related literature.Afterwards, thermally aware design processes are examined, followed by the application of machine learning models in EDA, and specifically in thermal analysis.The following chapter focuses on benchmark datasets and datasets used in the training of these models, and a synthetic dataset is introduced that allows for the training of generalizable machine learning models.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».