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Significant success has been reported recently using deep neural networks forclassification. Such large networks can be computationally intensive, evenafter training is over. Implementing these trained networks in hardware chipswith a limited precision of synaptic weights may improve their speed and energyefficiency by several orders of magnitude, thus enabling their integration intosmall and low-power electronic devices. With this motivation, we develop acomputationally efficient learning algorithm for multilayer neural networkswith binary weights, assuming all the hidden neurons have a fan-out of one.This algorithm, derived within a Bayesian probabilistic online setting, isshown to work well for both synthetic and real-world problems, performingcomparably to algorithms with real-valued weights, while retainingcomputational tractability.

Mean Field Bayes Backpropagation: scalable training of multilayer neural networks with binary weights