NOVEL TEST GENERATION ALGORITHM FOR COMBINATION CIRCUITS

2000 ◽  
Vol 10 (01n02) ◽  
pp. 27-65 ◽  
Author(s):  
K. RAAHEMIFAR ◽  
M. AHMADI
Keyword(s):  
Fault Simulation ◽  
Solution Space ◽  
Mapping Function ◽  
Entire Solution ◽  
Logic Circuit ◽  
Combinational Circuits ◽  
Test Set ◽  
Multiple Faults ◽  
Function Number ◽  
Test Vectors

It has been known for many years that combinational circuits have a Complete Test Set (CTS) which is capable of detecting all single and multiple faults. In this paper, we attempt to find CTS systematically. Our algorithm finds a test set which detects all single and multiple stuck-at faults in combinational circuits. This test set is obtained without probing internal nodes, using fault simulation or fault enumeration. It is shown that the test set is independent of logic circuit structure and dependent to the mapping function, number of inputs, outputs, and fanout stems. An upper-bound and lower-bound figures for the number of test vectors required to obtain 100% fault coverage are provided. This number is a small fraction of the entire solution space. A number of recommendations are made to improve the testability of a logic circuit.

Fault Detection Probability Evaluation Approach in Combinational Circuits Using Test Set Generation Method

2018 ◽  
Author(s):  
Namita Arya ◽  
Amit Prakash Singh
Keyword(s):  
Fault Detection ◽  
Maximum Error ◽  
Mass Function ◽  
Cumulative Distribution ◽  
Probability Mass Function ◽  
Combinational Circuits ◽  
Occurrence Probability ◽  
Test Set ◽  
Probability Of Error ◽  
Multiple Faults

This paper introduces an approach that chooses the fault detection by calculating probabilities using probability mass function (pmf) and cumulative distribution function (CDF). This work used a method for multiple stuck-at faults by producing a new test pattern in combinational circuits. We assumed that existence of all multiple faults is only because of one single component that is faulty. A complete test set can be created by all possible single stuck-at faults in a combinational circuit using some combination of gates. The test set generation fault detection method is applied on two different 3-bit input variable and 4-bit input variable circuits. The probability of error occurrence is calculated at both 3-bit and 4-bit input variable circuits. The resulting feature is used to obtain maximum error occurrence probability to detect faults by the logic used that the complexity of the circuit is inversely proportional to the fault occurrence probability. Then again, undetectability is directly proportional to the complexity of the circuit. Therefore, finest feasible circuit should have large input variable components with less complexity to reduce the fault occurrence probability.

Test Set Generation for Multiple Faults in Boolean Systems using SAT Solver

2021 ◽  
Author(s):  
P Partha Koundinya ◽  
Sai Krishna Reddy Y ◽  
V Mani Deepak ◽  
K Rutwesh ◽  
Anuj Deshpande
Keyword(s):  
Test Set ◽  
Multiple Faults ◽  
Sat Solver

scholarly journals Perturbation Based Learning for Structured NLP Tasks with Application to Dependency Parsing

2019 ◽  
Vol 7 ◽  
pp. 643-659
Author(s):  
Amichay Doitch ◽  
Ram Yazdi ◽  
Tamir Hazan ◽  
Roi Reichart
Keyword(s):  
Prediction Models ◽  
Learning Algorithm ◽  
Solution Space ◽  
Optimal Strategies ◽  
Expressive Power ◽  
Entire Solution ◽  
Dependency Parsing ◽  
Computationally Efficient ◽  
Cross Lingual

The best solution of structured prediction models in NLP is often inaccurate because of limited expressive power of the model or to non-exact parameter estimation. One way to mitigate this problem is sampling candidate solutions from the model’s solution space, reasoning that effective exploration of this space should yield high-quality solutions. Unfortunately, sampling is often computationally hard and many works hence back-off to sub-optimal strategies, such as extraction of the best scoring solutions of the model, which are not as diverse as sampled solutions. In this paper we propose a perturbation-based approach where sampling from a probabilistic model is computationally efficient. We present a learning algorithm for the variance of the perturbations, and empirically demonstrate its importance. Moreover, while finding the argmax in our model is intractable, we propose an efficient and effective approximation. We apply our framework to cross-lingual dependency parsing across 72 corpora from 42 languages and to lightly supervised dependency parsing across 13 corpora from 12 languages, and demonstrate strong results in terms of both the quality of the entire solution list and of the final solution. 1

scholarly journals Estimating the Density of States of Boolean Satisfiability Problems on Classical and Quantum Computing Platforms

2020 ◽  
Vol 34 (02) ◽  
pp. 1627-1635 ◽  
Author(s):  
Tuhin Sahai ◽  
Anurag Mishra ◽  
Jose Miguel Pasini ◽  
Susmit Jha
Keyword(s):  
Density Of States ◽  
Solution Space ◽  
Conjunctive Normal Form ◽  
Entire Solution ◽  
Concentration Of Measure ◽  
Problem Instance ◽  
Boolean Formula ◽  
Exact Answer ◽  
Novel Approach ◽  
Max Sat

Given a Boolean formula ϕ(x) in conjunctive normal form (CNF), the density of states counts the number of variable assignments that violate exactly e clauses, for all values of e. Thus, the density of states is a histogram of the number of unsatisfied clauses over all possible assignments. This computation generalizes both maximum-satisfiability (MAX-SAT) and model counting problems and not only provides insight into the entire solution space, but also yields a measure for the hardness of the problem instance. Consequently, in real-world scenarios, this problem is typically infeasible even when using state-of-the-art algorithms. While finding an exact answer to this problem is a computationally intensive task, we propose a novel approach for estimating density of states based on the concentration of measure inequalities. The methodology results in a quadratic unconstrained binary optimization (QUBO), which is particularly amenable to quantum annealing-based solutions. We present the overall approach and compare results from the D-Wave quantum annealer against the best-known classical algorithms such as the Hamze-de Freitas-Selby (HFS) algorithm and satisfiability modulo theory (SMT) solvers.

Sign in / Sign up

Export Citation Format

Share Document