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Browsing by Author "Chandrasekar, Kameshwar"

Now showing 1 - 3 of 3
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    ATPG based Preimage Computation: Efficient Search Space Pruning using ZBDD
    Chandrasekar, Kameshwar (Virginia Tech, 2003-07-28)
    Preimage Computation is a fundamental step in Formal Verification of VLSI designs. Conventional OBDD-based methods for Formal Verification suffer from spatial explosion, since large designs can blow up in terms of memory. On the other hand, SAT/ATPG based methods are less demanding on memory. But the run-time can be huge for these methods, since they must explore an exponential search space. In order to reduce this temporal explosion of SAT/ATPG based methods, efficient learning techniques are needed. Conventional ATPG aims at computing a single solution for its objective. In preimage computation, we must enumerate all solutions for the target state during the search. Similar sub-problems often occur during preimage computation that can be identified by the internal state of the circuit. Therefore, it is highly desirable to learn from these search-states and avoid repeated search of identical solution/conflict subspaces, for better performance. In this thesis, we present a new ZBDD based method to compactly store and efficiently search previously explored search-states. We learn from these search-states and avoid repeating subsets and supersets of previously encountered search spaces. Both solution and conflict subspaces are pruned based on simple set operations using ZBDDs. We integrate our techniques into a PODEM based ATPG engine and demonstrate their efficiency on ISCAS '89 benchmark circuits. Experimental results show that upto 90% of the search-space is pruned due to the proposed techniques and we are able to compute preimages for target states where a state-of-the-art technique fails.
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    Decision selection and associated learning for computing all solutions in automatic test pattern generation (ATPG) and satisfiability
    (United States Patent and Trademark Office, 2008-04-08)
    An all solutions automatic test pattern generation (ATPG) engine method uses a decision selection heuristic that makes use of the “connectivity of gates” in the circuit in order to obtain a compact solution-set. The “symmetry in search-states” is analyzed using a “Success-Driven Learning” technique which is extended to prune conflict sub-spaces. A metric is used to determine the use of learnt information a priori, which information is stored and used efficiently during “success driven learning”.
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    Search-space Aware Learning Techniques for Unbounded Model Checking and Path Delay Testing
    Chandrasekar, Kameshwar (Virginia Tech, 2006-04-14)
    The increasing complexity of VLSI designs, in recent years, poses serious challenges while ensuring the correctness of large designs for functionality and timing. In this dissertation, we target two related problems in Design Verification and Testing: Unbounded Model Checking and Path Delay Fault Testing, that commonly suffer from extremely large memory requirements. We propose efficient representations and intelligent learning techniques that reason on the problem structure and take advantage of the repeated search space, thereby alleviating the memory required and time taken to solve these problems. In this dissertation, we exploit Automatic Test Pattern Generation (ATPG) for Unbounded Model Checking (UMC). In order to perform unbounded model checking, we need the core image / preimage computation engines that perform forward / backward reachability analysis. First, we develop an ATPG engine, with search-space aware learning, that computes ``all solutions" for a given target objective and stores it as a decision diagram. We propose efficient decision selection heuristics and derive a suitable cut-set metric to quickly obtain a compact solution set. The solution set that is obtained, with the initial state set as the objective, represents the one-cycle preimage. In order to use the preimage state set as the objective in the subsequent iterations, we propose efficient techniques to convert a decision diagram into clauses/circuit. We propose a node-based conversion scheme that derives the functionality of each node in the decision diagram. The proposed scheme contains the size of the state set and helps to iteratively compute the preimage for many cycles until a fixed point / desired state is reached. Further, we gear the ATPG engine to directly compute the circuit cofactors, rather than individual solutions. The circuit cofactors contain a large number of solutions and hence capture a larger solution space. We also propose efficient learning techniques to prune the cofactor space and accelerate preimage computation. Then, we develop an exclusive image computation procedure that branches on the combinational inputs of the circuit and projects the values on the next state flip-flops as the image. We perform learning on the input solution space and incrementally store the image obtained as a decision diagram. We consistently show, with our experimental results, that our techniques are better than the existing techniques in terms of both performance and capacity. In the case of delay testing, we consider the test generation for path delay fault (PDF) model, which is the most accurate in characterizing the cumulative effect of distributed delays along each path in a circuit. The main bottle-neck in the ATPG for PDFs is the exponential number of paths in a circuit. In this work, we use the circuit information to analyze the common segments shared by different paths in a circuit. Based on the common sensitization constraints, we propose to identify the ``untestable core of segments" that cannot be sensitized together. We use these segments to identify the conflict search space for a huge number of untestable path delay faults apriori and prune them on-the-fly during test generation. Experimental results show that a huge number of untestable path delay faults are identified and it helps to accelerate test generation.