Statistical learning in chip (SLIC)

Published

Conference Paper

© 2015 IEEE. Despite best efforts, integrated systems are born (manufactured) with a unique 'personality' that stems from our inability to precisely fabricate their underlying circuits, and create software a priori for controlling the resulting uncertainty. It is possible to use sophisticated test methods to identify the best-performing systems but this would result in unacceptable yields and correspondingly high costs. The system personality is further shaped by its environment (e.g., temperature, noise and supply voltage) and usage (i.e., the frequency and type of applications executed), and since both can fluctuate over time, so can the system's personality. Systems also grow old and degrade due to various wear-out mechanisms (e.g., negative-bias temperature instability), and unexpectedly due to various early-life failure sources. These nature and nurture influences make it extremely difficult to design a system that will operate optimally for all possible personalities. To address this challenge, we propose to develop statistical learning in-chip (SLIC). SLIC is a holistic approach to integrated system design based on continuously learning key personality traits on-line, for self-evolving a system to a state that optimizes performance hierarchically across the circuit, platform, and application levels. SLIC will not only optimize integrated-system performance but also reduce costs through yield enhancement since systems that would have before been deemed to have weak personalities (unreliable, faulty, etc.) can now be recovered through the use of SLIC.

Full Text

Duke Authors

Cited Authors

  • Blanton, RD; Li, X; Mai, K; Marculescu, D; Marculescu, R; Paramesh, J; Schneider, J; Thomas, DE

Published Date

  • January 5, 2016

Published In

  • 2015 Ieee/Acm International Conference on Computer Aided Design, Iccad 2015

Start / End Page

  • 664 - 669

International Standard Book Number 13 (ISBN-13)

  • 9781467383882

Digital Object Identifier (DOI)

  • 10.1109/ICCAD.2015.7372633

Citation Source

  • Scopus