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This paper points to an abnormal phenomena of comparator networks. For most key processing problems (such as sorting, merging or insertion) the smaller the input size the easier the problem. Surprisingly, this is not the case for Bitonic sorting. Namely, the minimal depth of a comparator network that sorts all Bitonic sequences of n keys is not monotonic in(More)
The 0-1 Principle of Knuth and its many variants are well-known in the context of comparator networks. However, the comparator model is not the strongest model of computation obeying such principles. This paper studies another natural model of computation, the Min-Max model, that obeys all known 0-1 Principles. More important, it is the strongest model(More)
A set of input vectors S is conclusive for a certain functionality if, for every comparator network, correct functionality for all input vectors is implied by correct functionality for all vectors in S. We consider four functionalities of comparator networks: sorting, merging, sorting of bitonic vectors, and halving. For each of these functionalities, we(More)
Akçakaya, R. (3) Ayres, M. P. Bjørnstad, O. N. (2) Ettterson, J. (2) Ezoe, H. Guillot, G. Hanski, I. Hori, M. Hsieh, C.-H. (2) Itino, T. (2) Jovani, R. (2) Kameyama, Y. (3) Kelly, D. Kenta, T. Kishida, O. (4) Kondoh, M. Lambin, X. (2) Liebhold, A. M. (4) Maki, M. (2) Matsuura, K. (3) Miki, T. (2) Miyatake, T. (2) Morita, K. (2) Nakaoka, M. (4) Ovaskainen,(More)
This study explores the feasibility of stochastic neuron simulation in digital systems (FPGA), which realizes an implementation of a two-dimensional neuron model. The stochasticity is added by a source of current noise in the silicon neuron using an Ornstein-Uhlenbeck process. This approach uses digital computation to emulate individual neuron behavior(More)