Threshold circuits of bounded depth
Journal of Computer and System Sciences
On Optimal Depth Threshold Circuits for Multiplication andRelated Problems
SIAM Journal on Discrete Mathematics
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
A linear lower bound on the unbounded error probabilistic communication complexity
Journal of Computer and System Sciences - Complexity 2001
Depth-Efficient Threshold Circuits for Multiplication and Symmetric Function Computation
COCOON '96 Proceedings of the Second Annual International Conference on Computing and Combinatorics
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
On the complexity of depth-2 circuits with threshold gates
MFCS'05 Proceedings of the 30th international conference on Mathematical Foundations of Computer Science
Energy and depth of threshold circuits
Theoretical Computer Science
Energy-efficient threshold circuits computing mod functions
CATS '11 Proceedings of the Seventeenth Computing: The Australasian Theory Symposium - Volume 119
Energy-efficient threshold circuits computing mod functions
CATS 2011 Proceedings of the Seventeenth Computing on The Australasian Theory Symposium - Volume 119
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In the 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 then prove that the function f can be computed also by a threshold circuit C′ of 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.