1. 6/11/2015A Fault-Independent etc…1 A Fault-Independent Transitive Closure Algorithm for Redundancy Identification Vishal J. Mehta Kunal K. Dave Vishwani D. Agrawal Michael L. Bushnell ECE Dept., Rutgers University Piscataway, New Jersey, USA

2. 6/11/2015A Fault-Independent etc…2 Talk Outline Problem statement Background Implication graph Partial implications Transitive closure Redundancy identification Node fixation Results Conclusion

3. 6/11/2015A Fault-Independent etc…3 Problem Statement We make significant improvements in Redundancy identification of combinational circuits using partial implications and transitive closure. The new techniques have many other applications.

4. 6/11/2015A Fault-Independent etc…4 Background Implication graphs: Chakradhar, et al., Book, 1990 Larrabee, IEEE-TCAD, 1992 Zhao, et al., IEEE-VTS, 1997 Transitive closure: ATPG: Chakradhar, et al., IEEE-TCAD, 1993 Redundancy, Agrawal, et al., ATS, 1996 Partial implications: Henftling, et al., ECAD, 1995 Gaur, et al., DELTA, 2002

5. 6/11/2015A Fault-Independent etc…5 Implication graph Nodes Two nodes per signal; nodes a and a correspond to signal a. A node has two states (true,false); represents the signal state. Edges A directed edge from node a to b means “a=1” implies “b=1”. An implication graph is a representation of logical implications between pairs of signals of a digital circuit.

6. 6/11/2015A Fault-Independent etc…6 Building an Implication Graph » If C is ‘1’ then that implies that A and B must be ‘1’, but the reverse is not true. Similarly, if either A or B is ‘0’ then C will be ‘0’. But if we want to represent the implications of A and B on C then partial implications are necessary. ABAB C AC + BC + ABC = 0 AB + C = 0 A BC A BC

7. 6/11/2015A Fault-Independent etc…7 Partial Implications ABAB C AC + BC + ABC = 0 AB + C = 0 Reference: Henftling, et al., EDAC, 1995 A BC A BC  ANDing Node

8. 6/11/2015A Fault-Independent etc…8 Observability Variables Observability variable of a signal represents whether or not that signal is observable at a PO. It can be true or false. O C O A + BO A + O C BO A = 0 O C B + O A = 0 ABAB C OBOB OAOA O C = 1 (PO) B OAOA OCOC Reference: Agrawal et al., ATS’96

9. 6/11/2015A Fault-Independent etc…9 Adding Observability Variables to Implication Graph O C O A + BO A + O C BO A = 0 B OAOA OCOC A BC A BC OCOC OAOA OCOC OAOA O B can be added similarly.

10. 6/11/2015A Fault-Independent etc…10 Transitive Closure Transitive closure of a directed graph contains the same set of nodes as the original graph. If there is a directed path from node a to b, then the transitive closure contains an edge from a to b. ab c d a b cd A graph Transitive closure

11. 6/11/2015A Fault-Independent etc…11 Stuck-at Faults This is a type of fault, which causes a line to hold a constant logic value, irrespective of change of state at previous stages. There are two types of stuck-at-faults: Stuck-at-1 Stuck-at-0 Detection of a fault requires the fault to be activated and its effect observed at a PO. Fault a s-a-1 is detectable, if following conditions are simultaneously satisfied: a = 0 O a = 1

12. 6/11/2015A Fault-Independent etc…12 Redundant Faults A fault that has no test is called an untestable fault. Any untestable fault in a combinational circuit is a redundant fault because it does not cause any change in the input/output logic function of the circuit. Identification of redundant faults is useful because they can be removed from testing consideration, or from hardware

13. 6/11/2015A Fault-Independent etc…13 Redundancy Identification ATPG based methods Use exhaustive test pattern generation to determine whether or not a target fault has a test. All redundant faults can be found, but the ATPG cost is high (exponential in circuit size). Fault independent methods Analyze circuit topology and function locally without targeting a specific fault. Less complex than ATPG, e.g., testability analysis. Many (not all) redundant faults can be found at a lower cost.

14. 6/11/2015A Fault-Independent etc…14 Redundancy Identification by Transitive Closure a b c d e s-a-0 Implication graph (some nodes and edges not shown) Circuit with two redundant faults Implication Partial implication Transitive closure edge a bc d OcOc OdOd

15. 6/11/2015A Fault-Independent etc…15 Method Summarized Obtain an implication graph from the circuit topology and compute transitive closure. There are 8 different conditions on the basis of which a fault is said to be redundant. Examples: If node c implies c then s-a-0 fault on line c is redundant. If node O c implies O c then c is unobservable and both s-a-0 and s-a-1 faults on line c are redundant.

16. 6/11/2015A Fault-Independent etc…16 Graph Size and Complexity Direct Implications:  k i=1 (2n i + 2n i 2 ) ~ O(k) Partial Implications:  k i=1 (n i + 2n i 2 + n i 3 ) ~ O(k) Controllability nodes: 2[ #PI + k + #PO] ~ O(k) Observability nodes: 2[#PI + k + #PO +  k i=1 #fanout branches] ~ O(k) n : number of inputs for the i th gate. k : number of gates in the given circuit. Time complexity for computing transitive closure is O(k 3 ), but Gaur et al. (2002) show that empirically it has linear complexity.

17. 6/11/2015A Fault-Independent etc…17 Node Fixation Node fixation occurs when a signal implies its own complement, or vice-versa. Edges from all other nodes are added in the implication graph to model the unconditional fixation.

18. 6/11/2015A Fault-Independent etc…18 Example - Node Fixation Initially only 2 out of 7 redundant faults were identified. After the implementation of node fixation concept, g-(s-a-1) was identified. e fg e fg Note: Only some edges are shown s-a-1 e f g s-a-0 s-a-1 s-a-0 s-a-1

19. 6/11/2015A Fault-Independent etc…19 Contrapositive Rule If a signal p implies another signal q then q implies p (Zhao et al. VTS’97). This rule gives more implications in the graph after the node fixation is implemented and we are yet to verify how many more redundant faults will be found.

20. 6/11/2015A Fault-Independent etc…20 Benchmark Results Circuit C3540 S9234 s13207 Total Flts. 3428 6927 9815 ATPG Flts. CPU s 131 24.6 452 803.7 151 806.5 TC/par.imp. Flts. CPU s 110 2.7 235 13.5 74 39.0 TC Flts. CPU s 54 5.9 16 9.8 9 11.2 FIRE Flts. CPU s 93 11.9 165 20.6 55 23.2 Identified redundant faults and computation time ATPG: TRAN, Chakradhar et al., IEEE-TCAD’93, Sparc 5 TC/par.imp.: This paper, Sparc 5 TC: Agrawal et al., ATS’96, Sparc 5 FIRE: Iyer and Abramovici, IEEE-TVLSI’96, Sparc 2

21. 6/11/2015A Fault-Independent etc…21 Limitation of Method Observability variable of a fanout stem is not analyzed. Only the redundant faults due to false controllability of fanout stem can be identified. Three redundant s-a-0 faults identified by transitive closure (uncontrollable signals) s-a-0 s-a-1 Two redundant stem faults not identified by transitive closure (unobservable stem)

22. 6/11/2015A Fault-Independent etc…22 Conclusion Partial implications improve fault- independent redundancy identification – present results are the best known. Transitive closure computation run times were linear in the number of nodes for benchmark circuits (Gaur et al., DELTA’02) -- the known worst-case complexity is O(N 3 ) for N nodes. Further work has shown that many unobservable fanout stems can be identified from transitive closure analysis.

23. 6/11/2015A Fault-Independent etc…23 THANK YOU