This paper presents a new feedback shift register-based method for embedding deterministic test patterns on-chip suitable for complementing conventional BIST techniques for in-field testing. Our experimental results on 8 real designs show that the presented approach outperforms the bit-flipping approach by 24.7 on average. We also show that it is possible to exploit the uneven distribution of don't care bits in test patterns in order to reduce the area required for storing deterministic test patterns more than 3 times with less than 2 fault coverage drop.
The first algorithm for constructing a binary machine with the minimum number of stages for a given binary sequence was presented in @cite_19 . This algorithm exploits the unique property of binary machines that any binary @math -tuple can be the next state of a given current state. The algorithm assigns every 0 of a sequence a unique even integer and every 1 of a sequence a unique odd integer. Integers are assigned in an increasing order starting from 0. For example, if an 8-bit sequence 00101101 is given, the sequence of integers 0,2,1,4,3,5,6,7 can be used. This sequence of integers is interpreted as a sequence of states of a binary machine. The largest integer in the sequence of states determines the number of stages. In the example above, @math , thus the resulting binary machine has 3 stages. The feedback functions @math implementing the resulting current-to-next state mapping are derived using the traditional logic synthesis techniques @cite_26 .
@cite_19 @cite_26
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The problem of constructing a binary machine with the minimum number of stages generating a given binary sequence is addressed. Binary machines are a generalization of nonlinear feedback shift registers (NLFSRs) in which both connections, feedback and feedforward, are allowed and no chain connection between the register stages is required. An algorithm for constructing a shortest binary machine generating a given periodic binary sequence is presented. The paper describes techniques for constructing statistically independent binary sequences with prescribed ratios of zeros and ones. The first construction is a general recursive construction, which forms the sequences from a class of "elementary" sequences. The second construction is a special construction which can be used when the ratio of ones to zeros is expressed in binary notation. The second construction is shown to be optimal in terms of the numbers of input sequences required to construct the desired sequence. The paper concludes with a discussion of how to generate independent "elementary" sequences using simple digital techniques. > We study a natural generalization of the problem of minimizing makespan on unrelated machines in which jobs may be split into parts. The different parts of a job can be (simultaneously) processed on different machines, but each part requires a setup time before it can be processed. First we show that a natural adaptation of the seminal approximation algorithm for unrelated machine scheduling [11] yields a 3-approximation algorithm, equal to the integrality gap of the corresponding LP relaxation. Through a stronger LP relaxation, obtained by applying a lift-and-project procedure, we are able to improve both the integrality gap and the implied approximation factor to 1 + φ, where φ ≈ 1.618 is the golden ratio. This ratio decreases to 2 in the restricted assignment setting, matching the result for the classic version. Interestingly, we show that our problem cannot be approximated within a factor better than e e-1 ≈ 1.582 (unless P = NP). This provides some evidence that it is harder than the classic version, which is only known to be inapproximable within a factor 1.5 - e. Since our 1 + φ bound remains tight when considering the seemingly stronger machine configuration LP, we propose a new job based configuration LP that has an infinite number of variables, one for each possible way a job may be split and processed on the machines. Using convex duality we show that this infinite LP has a finite representation and can be solved in polynomial time to any accuracy, rendering it a promising relaxation for obtaining better algorithms. © 2014 Springer International Publishing Switzerland. This paper studies a generalization of the classic binary search problem of locating a desired value within a sorted list. The classic problem can be viewed as determining the correct one-dimensional, binary-valued threshold function from a finite class of such functions based on queries taking the form of point samples of the function. The classic problem is also equivalent to a simple binary encoding of the threshold location. This paper extends binary search to learning more general binary-valued functions. Specifically, if the set of target functions and queries satisfy certain geometrical relationships, then an algorithm, based on selecting a query that is maximally discriminating at each step, will determine the correct function in a number of steps that is logarithmic in the number of functions under consideration. Examples of classes satisfying the geometrical relationships include linear separators in multiple dimensions. Extensions to handle noise are also discussed. Possible applications include machine learning, channel coding, and sequential experimental design.