z-logo
open-access-imgOpen Access
Leveraging design diversity to counteract process variation: theory, method, and FPGAtoolchain to increase yield and resiliencein‐situ
Author(s) -
Alzahrani Ahmad A.,
DeMara Ronald F.
Publication year - 2019
Publication title -
iet computers and digital techniques
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.219
H-Index - 46
eISSN - 1751-861X
pISSN - 1751-8601
DOI - 10.1049/iet-cdt.2018.5012
Subject(s) - yield (engineering) , diversity (politics) , process variation , in situ , variation (astronomy) , process (computing) , computer science , reliability engineering , engineering , chemistry , materials science , sociology , physics , operating system , organic chemistry , anthropology , astrophysics , metallurgy
With continued scaling of integrated circuits into deep nanoscale fabrication technologies, the aggravated effects of reliability degradation and variability in process parameters can hinder effective yields. Fortunately, due to the immense flexibility of contemporary reconfigurable hardware (RH), reconfiguration‐based resilience can be exploited to effectively tackle such challenges. Nonetheless, reconfiguration‐based resiliency is typically limited due to the complexity of the fault resolution space, interconnect routing constraints, and dynamic reconfiguration time in situ. These challenges are addressed herein by deriving a pre‐emptive design approach based on union‐free hypergraphs, which can define distinct physical implementations with highly separable subsets of the target device's resources covering the largest solution space feasible for reliability exposures and uncertain parametric variations. Two scalable and highly transportable algorithms to realise union‐free hypergraphs are introduced and investigated. Hardware demonstration on a commercial‐grade field programmable gate array platform shows a significant increase in fault tolerance compared to commonly‐used modular redundancy methods. Furthermore, Monte‐Carlo statistical results across a set of benchmarks show an average improvement in critical path delay of 6.8, 8.6, and 10.8% for combined variations of 15, 25, and 35%, respectively, while achieving a net reduction in performance variation impact of 34.8, 38, and 41% for identical levels of variability.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.
Having issues? You can contact us here