NDLI: On Using Design Partitioning to Reduce Diagnosis Memory Footprint

Content Provider	IEEE Xplore Digital Library
Author	Xiaoxin Fan Huaxing Tang Reddy, S.M. Wu-Tung Cheng Benware, B.
Copyright Year	2011
Abstract	Recently statistical yield learning based on volume diagnosis has become popular. Volume diagnosis requires a large amount of diagnosis results to be produced within a reasonable time. However, it is challenging to achieve the desired throughput for modern designs with continuously increasing size. In this paper, we propose a method to partition a design under diagnosis into blocks together with a diagnosis flow at the block level. The diagnosis throughput is improved because more diagnosis jobs can be run concurrently and each job runs faster due to the reduced memory. A measure is also proposed to estimate the impact on diagnosis caused by design partitioning. Experimental results on benchmark circuits and several industrial designs show that diagnosis using circuit blocks has minimal impact on diagnosis accuracy and resolution. It is also demonstrated that the proposed measure is a good metric in predicting the impact on diagnosis.
Starting Page	219
Ending Page	225
File Size	265371
Page Count	7
File Format	PDF
ISBN	9781457719844
ISSN	10817735
DOI	10.1109/ATS.2011.45
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-11-20
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Logic gates Circuit faults Partitioning algorithms Algorithm design and analysis Dictionaries Bismuth Accuracy logic diagnosis yield learning volume diagnosis design partitioning
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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