Neural Comput - Energy complexity of recurrent neural networks.

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Resumo

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 [Formula: see text] with the time overhead of [Formula: see text] per one input bit, using the energy O(e), for any e such that [Formula: see text] and e=O(s), which shows the time-energy trade-off in recurrent networks. In addition, for the time overhead [Formula: see text] satisfying [Formula: see text], we obtain the lower bound of [Formula: see text] on the energy of such a simulation for some constant c>0 and for infinitely many s.

Resumo Limpo

recent new socal energi complex measur introduc studi feedforward perceptron network measur inspir fact biolog neuron requir energi transmit spike fire activ neuron brain quit spars neuron fire letter investig energi complex recurr network count number activ neuron time instant comput prove determinist finit automaton m state can simul neural network optim size formula see text time overhead formula see text per one input bit use energi oe e formula see text eo show timeenergi tradeoff recurr network addit time overhead formula see text satisfi formula see text obtain lower bound formula see text energi simul constant c infinit mani s

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