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- W3149666044 abstract "Recently a new so-called energy complexity measure has been introduced and studied for feedforward perceptron networks. This measure is inspired by the fact that biological neurons require more energy to transmit a spike than not to fire, and the activity of neurons in the brain is quite sparse, with only about 1% of neurons firing. In this letter, we investigate the energy complexity of recurrent networks, which counts the number of active neurons at any time instant of a computation. We prove that any deterministic finite automaton with m states can be simulated by a neural network of optimal size s = �( √ m) with the time overhead of τ = O(s/e) per one input bit, using the energy O(e) ,f or anye such that e = �( logs) and e = O(s), which shows the time-energy trade-off in recurrent networks. In addition, for the time overhead τ satisfying τ τ = o(s), we obtain the lower bound of s c/τ on the energy of such a simulation for some constant c > 0 and for infinitely many s." @default.
- W3149666044 created "2021-04-13" @default.
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- W3149666044 date "2014-01-01" @default.
- W3149666044 modified "2023-09-27" @default.
- W3149666044 title "Energy Complexity of Recurrent Neural Networks" @default.
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