Circuit complexity and neural networks
Circuit complexity and neural networks
Discrete neural computation: a theoretical foundation
Discrete neural computation: a theoretical foundation
Size--Depth Tradeoffs for Threshold Circuits
SIAM Journal on Computing
On the computational power of threshold circuits with sparse activity
Neural Computation
Theoretical Computer Science
Size and Energy of Threshold Circuits Computing Mod Functions
MFCS '09 Proceedings of the 34th International Symposium on Mathematical Foundations of Computer Science 2009
Energy complexity and depth of threshold circuits
FCT'09 Proceedings of the 17th international conference on Fundamentals of computation theory
Rational approximation techniques for analysis of neural networks
IEEE Transactions on Information Theory
Size-energy tradeoffs for unate circuits computing symmetric Boolean functions
Theoretical Computer Science
Energy and fan-in of threshold circuits computing mod functions
TAMC'11 Proceedings of the 8th annual conference on Theory and applications of models of computation
Energy and fan-in of logic circuits computing symmetric Boolean functions
Theoretical Computer Science
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In this paper we show that there is a close relationship between the energy complexity and the depth of threshold circuits computing any Boolean function although they have completely different physical meanings. Suppose that a Boolean function f can be computed by a threshold circuit C of energy complexity e and hence at most e threshold gates in C output ''1'' for any input to C. We prove that the function f can also be computed by a threshold circuit C^' of the depth 2e+1 and hence the parallel computation time of C^' is 2e+1. If the size of C is s, that is, there are s threshold gates in C, then the size s^' of C^' is s^'=2es+1. Thus, if the size s of C is polynomial in the number n of input variables, then the size s^' of C^' is polynomial in n, too.