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PHDD: an efficient graph representation for floating point circuit verification
Full text Publisher SitePublisher Site PdfPdf (125 KB)
Source International Conference on Computer Aided Design archive
Proceedings of the 1997 IEEE/ACM international conference on Computer-aided design table of contents
San Jose, California, United States
Pages: 2 - 7  
Year of Publication: 1997
ISBN:0-8186-8200-0
Authors
Yirng-An Chen  Carnegie Mellon University, Pittsburgh, PA
Randal E. Bryant  Carnegie Mellon University, Pittsburgh, PA
Sponsors
SIGDA: ACM Special Interest Group on Design Automation
IEEE-CS : Computer Society
Publisher
IEEE Computer Society  Washington, DC, USA
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Downloads (6 Weeks): 1,   Downloads (12 Months): 11,   Citation Count: 11
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ABSTRACT

Data structures such as *BMDs, HDDs, and K*BMDs provide compact representations for functions which map Boolean vectors into integer values, but not floating point values. In this paper, we propose a new data structure, called Multiplicative Power Hybrid Decision Diagrams (*PHDDs), to provide a compact representation for functions that map Boolean vectors into integer or floating point values. The size of the graph to represent the IEEE floating point encoding is linear with the word size. The complexity of floating point multiplication grows linearly with the word size. The complexity of floating point addition grows exponentially with the size of the exponent part, but linearly with the size of the mantissa part. We applied *PHDDs to verify integer multipliers and floating point multipliers before the rounding stage, based on a hierarchical verification approach. For integer multipliers, our results are at least 6 times faster than *BMDs. Previous attempts at verifying floating point multipliers required manual intervention. We verified floating point multipliers before the rounding stage automatically.


REFERENCES

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CHEN, Y.-A., AND BRYANT, R. E. *PBHD: An efficient graph representation for floating point circuit verification. Tech. Rep. CMU-CS-97-134, School of Computer Science, Carnegie Mellon University, 1997.
 
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