M. Ganai, A. Aziz, and A. Kuehlmann, "Enhancing simulation with BDDs and ATPG," in Design Automation Conference, June 1999.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....For example, in Figure 7, signal x is set to symbol and signal g is the target signal with a desired value. The time expanded model is the shaded region. This analysis is performed using formal techniques such as Binary Decision Diagrams (BDDs) 2] and Automatic Test Pattern Generation (ATPG) [3]. In the BDD approach, the forward symbol propagation is first performed from the set signals to the target. Once the trimmed BDDs for the target signals are available, the remaining task is to extract the minterms in the BDDs, which satisfy the desired values of the target signal. In the ATPG ....

Ganai, Malay; Aziz, Adnan; Kuehlmann Andreas, "Enhancing Simulation with BDDs and ATPG", DAC 1999, p. 385-390.

.... of BDD based approaches is the memory explosion problem, which occurrs when the BDDs for representing the state transition functions of the circuits may not be constructed entirely [7] As a result, methods other than BDDs have been proposed, such as the ATPG based approach for model checking [8, 15, 14]. The ATPG based approach does not explicitly store the complete state space or build a canonical representation of the Boolean state transition function, instead, the state transition is done by forward simulation and the state information is learned on the y. A solution is derived when sucient ....

....space (memory) and could be limited in time if variable ordering is needed, while ATPG approaches are limited in time. In terms of sequential ATPG, two approaches are possible: deterministic and simulation based. Currently, almost all previous work on using ATPG for veri cation are deterministic [8, 15, 14] because they can prove correctness (i.e. when the modeled fault is untestable) In this case, a branch and bound algorithm is employed to exhaust the search space progressively so that no input vector could be found to detect the targeted fault; thus, we can safely claim that this property is ....

M. K. Ganai, A. Aziz, and A. Kuehlmann, \Enhancing Simulation with BDDs and ATPG", Proc. DAC, 1999.

....level is not clear, for example, concerning the effectiveness of the representation of the symbolic ternary values by decision diagrams. Furthermore, the restricted logic constrains the applicability to equivalence checking. Two related heuristics for sequential verification are proposed by [6, 10]. Numerical and symbolic simulation are combined in [6] In each clock cycle, parts of the inputs are tied automatically to constants (as in numerical simulation) while others get symbolic values. Graph explosion of the OBDDs is avoided because of the constant inputs while the number of test ....

....automatically to constants (as in numerical simulation) while others get symbolic values. Graph explosion of the OBDDs is avoided because of the constant inputs while the number of test vectors simulated in one time unit is significantly increased compared to numerical simulation. The objective of [10] is to find efficiently counterexamples to safety properties by using iteratively numerical simulation, OBDDs and ATPG. The circuit is simulated and nodes which remain unchanged are remarked. A heuristic solver using OBDDs and ATPG techniques with a defined computation limit, generates inputs ....

M. K. Ganai, A. Aziz, and A. Kuehlmann. Enhancing simulation with BDDs and ATPG. In DAC'99, 1999.

....manipulations [4] Conceptually, these approaches systematically explore all states reachable from the initial state. The computational complexity of symbolic search is enormous; as such it is limited to designs containing of the order of a hundred latches. 1. 1 Directed Search: SIVA Ganai et al. [2] present a stand alone tool, SIVA, which combines simulation with symbolic methods to form a robust method for state space search directed towards userspecified targets. The working of SIVA is described in Algorithm 1. The design is read into SIVA as a netlist of gates and latches. Targets are ....

....to find the input sequence which satisfy the target. if (u=49) a=1; if (u=21) b=1; if (u=131 a=1 b=1) c=1; s0 s1 s2 s3 s0,s1,s2,s3: controller states u: input vector a,b,c: latches Figure 1. Control flow illustrating the need for lighthouses. Ganai et al. [2] observed that lighthouses play a major role in generating input sequences for hard to cover targets. The drawback of using lighthouses is that the user has to manually examine the design to find them. This can be tedious, and takes away from the usefulness of SIVA. In addition, specifying an ....

[Article contains additional citation context not shown here]

M. Ganai, A. Aziz, and A. Kuehlmann. Enhancing Simulation with BDDs and ATPG. In Proc. of the Design Automation Conf., New Orleans, LA, June 1999.

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M. Ganai, A. Aziz, and A. Kuehlmann, "Enhancing simulation with BDDs and ATPG," in Design Automation Conference, June 1999.

....manipulations [4] Conceptually, these approaches systematically explore all states reachable from the initial state. The computational complexity of symbolic search is enormous; as such it is limited to designs containing of the order of a hundred latches. 1. 1 Directed Search: SIVA Ganai et al. [2] present a stand alone tool, SIVA, which combines simulation with symbolic methods to form a robust method for state space search directed towards userspecified targets. The working of SIVA is described as follows. The design is read into SIVA as a netlist of gates and latches. Targets are ....

....hard to find the input sequence which satisfy the target. s0 s1 s2 s0,s1,s2,s3: controller states u: input vector a,b,c: latches c=1; if (u=131 a=1 b=1) b=1; if (u=21) a=1; if (u=49) Figure 1. Control flow illustrating the need for lighthouses. Ganai et al. [2] observed that lighthouses play a major role in generating input sequences for hard to cover targets. The drawback of using lighthouses is that the user has to manually examine the design to find them. This can be tedious, and takes away from the usefulness of SIVA. In addition, specifying an ....

[Article contains additional citation context not shown here]

M. Ganai, A. Aziz, and A. Kuehlmann. Enhancing Simulation with BDDs and ATPG. In Proc. of the Design Automation Conf., New Orleans, LA, June 1999.

....symbolic simulation as compared to previous approaches, speci cally, 12, 87, 4] Using our approach we found real bugs in industrial designs which were previously undetected. Guided State Space Search Partial state exploration using guided techniques has become a subject of wide research [62, 13, 91, 43, 90]. Such techniques focus on methods to guide the exploration towards the target states. Guided state space search can use state 7 score boarding for an explicit or symbolic state exploration to nd a trajectory from a set of initial states to the target states. Several metrics have been proposed ....

....state space search can use state 7 score boarding for an explicit or symbolic state exploration to nd a trajectory from a set of initial states to the target states. Several metrics have been proposed based on Hamming distance [93] tracking [91] reachability probability [62] and lighthouses [43] which had limited success. We propose a rarity based metric for state prioritization that describes heuristically the goodness of states. We propose several techniques which use the metric for enhanced state space search. These techniques are based on latch toggle activity and latch support for ....

[Article contains additional citation context not shown here]

M. K. Ganai, A. Aziz, and A. Kuehlmann. Enhancing Simulation with BDDs and ATPG. In Proc. of the Design Automation Conf., New Orleans, LA, June 1999.

....elements. Left with little choice, practical veri ers have directed their e ort in improving the design coverage and nding bugs early in the designs. Partial state exploration focuses on methods to guide the exploration towards the target states. It has become a topic of wide interest lately [4, 5, 6, 7, 8]. Guided state space search can use state score boarding for an explicit or symbolic state exploration to nd a trajectory from a set of initial states to the target states. This work was supported by the SRC contract 2000 TJ 847. We propose a rarity based metric that describes heuristically ....

....User de ned hints are used to delay BDD explosion and simplify the transition relation. It requires user understanding of the algorithm design implements, and also predict BDD behavior. In semiformal based techniques, guided search is carried out using user de ned guide posts [6] and lighthouses [7]. In [6] target enlargement was carried out to increase the opportunity for the guided search for nding a path to the error state. However, as this process uses BDDbased pre image computation, it does not scale. In another semi formal technique [8, 9] the authors have proposed automatic ....

[Article contains additional citation context not shown here]

M. K. Ganai, A. Aziz, and A. Kuehlmann. Enhancing Simulation with BDDs and ATPG. In Proc. of the Design Automation Conf., New Orleans, LA, June 1999.

....has been taken to the veri cation of equivalence of gate level designs. It has proved to be extremely successful; in this manner, equivalence of million gate designs can be shown as a matter of course [2] Based on the above, we have developed SIVA (SImulation Veri cation with Augmentation) [4], a tool for checking safety properties on digital hardware designs. SIVA integrates simulation with symbolic techniques for vector generation. A high level description of SIVA is as follows: designs are speci ed as a network of gates and latches. The user speci es a set of target nodes ....

....in the net work, we keep its signature, i.e. a bit vector where the k th bit corresponds to the value of the node on the k th input vector. We determine which nodes have constant zero signatures. We then construct input vectors which attempt to justify these nodes at the given state (details in [4]) This is pictorially represented in Figure 1 where R i and D i represent random input and witness 1 vector respectively. Input vector generation is performed by a combination of SAT based ATPG and BDD building. When the ATPG BDDs show no vector exists, we go on to the next node. To keep the ....

[Article contains additional citation context not shown here]

Malay Ganai, Adnan Aziz, and Andreas Kuehlmann. Enhancing Simulation with BDDs and ATPG. In Proc. of the Design Automation Conf., June 1999.

....manipulations [4] Conceptually, these approaches systematically explore all states reachable from the initial state. The computational complexity of symbolic search is enormous; as such it is limited to designs containing of the order of a hundred latches. 1. 1 Directed Search: SIVA Ganai et al. [2] present a stand alone tool, SIVA, which combines simulation with symbolic methods to form a robust method for state space search directed towards userspecified targets. The working of SIVA is described as follows. The design is read into SIVA as a netlist of gates and latches. Targets are ....

....to find the input sequence which satisfy the target. s0 s1 s2 s3 s0,s1,s2,s3: controller states u: input vector a,b,c: latches c=1; if (u=131 a=1 b=1) b=1; if (u=21) a=1; if (u=49) Figure 1. Control flow illustrating the need for lighthouses. Ganai et al. [2] observed that lighthouses play a major role in generating input sequences for hard to cover targets. The drawback of using lighthouses is that the user has to manually examine the design to find them. This can be tedious, and takes away from the usefulness of SIVA. In addition, specifying an ....

[Article contains additional citation context not shown here]

M. Ganai, A. Aziz, and A. Kuehlmann. Enhancing Simulation with BDDs and ATPG. In Proc. of the Design Automation Conf., New Orleans, LA, June 1999.

No context found.

Malay Ganai, Adnan Aziz, and Andreas Kuehlmann. Enhancing simulation with BDDs and ATPG. In Proceedings of 36th Design Automation Conference, pages 385-390, 1999.

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