This paper describes an approach for intent classification and tagging on embedded devices, such as smart watches. We describe a technique to train neuronal networks where the final neuronal network weights are binary. This enables memory bandwidth optimized inference and efficient computation even on constrained/embedded platforms. The flow of the approach is as follows: tf-idf word selection method reduces the number of overall weights. Bag-of-Words features are used with a feedforward and recurrent neuronal network for intent classification and tagging, respectively. A novel double Gaussian based regularization term is used to train the network. Finally, the weights are almost clipped lossless to −1 or 1 which results in a tiny binary neuronal network for intent classification and tagging. Our technique is evaluated using a text corpus of transcribed and annotated voice queries. The test domain is "lights control". We compare the intent and tagging accuracy of the ultra-compact binary neuronal network with our baseline system. The novel approach yields comparable accuracy but reduces the model size by a factor of 16: from 160kB to 10kB.

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Abstract. The set of solutions of a Volterra equation in a Banach space with a Carathéodory kernel is proved to be an R δ , in particular compact and connected. The kernel is not assumed to be uniformly continuous with respect to the unknown function and the characterization is given in terms of a B 0 -space of continuous functions on a noncompact domain.