37,060 research outputs found
A logic for constant-depth circuits
Consider a family of boolean circuitsC1,C2,...,Cn,..., constructed by some uniform, effective procedure operating on inputn. Such a procedure provides a concise representation of a family of parallel algorithms for computing boolean values. A formula of first-order logic may also be viewed as a concise representation of a family of parallel algorithms for evaluating boolean functions. The parallelism is implicit in the quantification (a formula [for all]x(x) is true if and only if each of the formulas(a) is true, and all these formulas can be checked simultaneously), and universes of different sizes give rise to boolean functions with different numbers of inputs (the boolean values of the formula's predicates on various combinations of elements of the universe). This note presents an extended first-order logic designed to be exactly equivalent in expressiveness to polynomialsize, constant-depth, unbounded-fan-in circuits constructed by Turing machines of bounded computational complexity.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/24848/1/0000275.pd
Sublogarithmic uniform Boolean proof nets
Using a proofs-as-programs correspondence, Terui was able to compare two
models of parallel computation: Boolean circuits and proof nets for
multiplicative linear logic. Mogbil et. al. gave a logspace translation
allowing us to compare their computational power as uniform complexity classes.
This paper presents a novel translation in AC0 and focuses on a simpler
restricted notion of uniform Boolean proof nets. We can then encode
constant-depth circuits and compare complexity classes below logspace, which
were out of reach with the previous translations.Comment: In Proceedings DICE 2011, arXiv:1201.034
Neural computation of arithmetic functions
A neuron is modeled as a linear threshold gate, and the network architecture considered is the layered feedforward network. It is shown how common arithmetic functions such as multiplication and sorting can be efficiently computed in a shallow neural network. Some known results are improved by showing that the product of two n-bit numbers and sorting of n n-bit numbers can be computed by a polynomial-size neural network using only four and five unit delays, respectively. Moreover, the weights of each threshold element in the neural networks require O(log n)-bit (instead of n -bit) accuracy. These results can be extended to more complicated functions such as multiple products, division, rational functions, and approximation of analytic functions
Synthesis and Optimization of Reversible Circuits - A Survey
Reversible logic circuits have been historically motivated by theoretical
research in low-power electronics as well as practical improvement of
bit-manipulation transforms in cryptography and computer graphics. Recently,
reversible circuits have attracted interest as components of quantum
algorithms, as well as in photonic and nano-computing technologies where some
switching devices offer no signal gain. Research in generating reversible logic
distinguishes between circuit synthesis, post-synthesis optimization, and
technology mapping. In this survey, we review algorithmic paradigms ---
search-based, cycle-based, transformation-based, and BDD-based --- as well as
specific algorithms for reversible synthesis, both exact and heuristic. We
conclude the survey by outlining key open challenges in synthesis of reversible
and quantum logic, as well as most common misconceptions.Comment: 34 pages, 15 figures, 2 table
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