Machine Learning Based Negative-Bias Temperature Instability (Nbti) Detection and Mitigation for Heterogeneous Multi-Core Systems(1/2)

Project Details


As CMOS technology continuous scaling down, a single chip can perform complicated data processing. Heterogeneous Multi-core System(HMS) can provide higher performance by appropriately performing task-to-core assignment. On the other hand, aging effect has become one of the most drastic challenges in modern IC design. Negative-Bias Temperature Instability (NBTI) effect can result in increased threshold voltage of pMOS transistors and may lead to timing failure after circuit aging. To mitigate or tolerance NBTI, previous researches developed different design structures as well as optimization strategies. However, only a few studies focus on NBTI-induced problems on HMSs. Therefore, in the proposal, we want to deeply study these NBTI-induced problems on HMSs, and develop a machine learning based algorithm to detect the aging situation of different modules in the HMSs, and propose a system level NBTI mitigation strategy.Specifically, this proposal addresses on the following two problems:1.Using machine learning algorithm to deploy and calibrate aging sensors in different modules of HMSs.2.NBTI-aware HMS system lifetime extension strategyWe will use machine learning algorithm to appropriately deploy aging sensors, and then develop a task-to-core mapping algorithm to extend the HMS system lifetime.
Effective start/end date1/08/2031/07/21

UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):

  • SDG 12 - Responsible Consumption and Production
  • SDG 13 - Climate Action
  • SDG 17 - Partnerships for the Goals


  • Heterogeneous Multi-Core System(HMS)
  • Negative-Bias Temperature Instability (NBTI)
  • aging sensor
  • Generative Adversarial Network (GAN)
  • asymmetric aging


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