Aging-aware scheduling and binding in high-level synthesis considering workload effects

Es'haghi, Siavash; Eshghi, Mohammad

doi:10.1016/j.microrel.2019.113549

Cited by 1 publication

(1 citation statement)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As a direct consequence of this, computer-aided design (CAD) systems now extensively enable design techniques that are based on high-level synthesis (HLS) algorithms. HLS investigates the design space by using procedures like scheduling and binding [43]. Because of the growing significance of rapid transistor aging, it is important to consider the lifespan of circuits in the early phases of the design process.…”

Section: A Problem Definitionmentioning

confidence: 99%

A Machine Learning Based Reliability Analysis of Negative Bias Temperature Instability (NBTI) Compliant Design for Ultra Large Scale Digital Integrated Circuit

Singh,

Kalra

2023

JICS

View full text Add to dashboard Cite

NBTI is a key reliability challenge in nanoscale digital design, and it is vital to address it throughout the exploration of design space at high levels of abstraction in order to improve reliability. A prediction model of aging that is adequate for these levels ought to be faster. In addition to this, the model should be able to forecast the recently discovered stochastic consequences of growing older. The purpose of this study is to offer a model that is based on machine learning (ML) and can predict aging effects. After obtaining a training set of sufficient size using Synopsis HSPICE (MOSFET Reliability, MOSRA) in the beginning, the machine-learning-based model is then trained and built in order to forecast the aging statistical features. Evaluation is done on a number of machine learning techniques, including Adaptive Neuro-Fuzzy Inference System (ANFIS), K-Nearest Neighbor (KNN), Support Vector Machine (SVM) and Random Forest (RF). The findings indicate that ANFIS algorithms are very effective in the process of age prediction. The proposed technique shows that the aging prediction runtime is reduced by more than 99% when compared to the MOSRA-based approach, and accurate predictions of the statistical properties of aging are obtained with an accuracy of more than 99% on complementary metal oxide semiconductor (CMOS) and metal gate/high-K (MGK) circuits at the 22nm technology node.

show abstract

Section: A Problem Definitionmentioning

confidence: 99%