Associative memories are data structures that allow retrieval of previously stored messages given part of their content. They thus behave similarly to human brain's memory that is capable, for instance, of retrieving the end of a song given its beginning. Among different families of associative memories, sparse ones are known to provide the best efficiency (ratio of the number of bits stored to that of bits used). Recently, a new family of sparse associative memories achieving almost- optimal efficiency has been proposed. Their structure , relying on binary connections and neurons, induces a direct mapping between input messages and stored patterns. Nevertheless, it is well known that non-uniformity of the stored messages can lead to dramatic decrease in performance. In this work, we show the impact of non-uniformity on the performance of this recent model and we exploit the structure of the model to improve its performance in practical applications where data is not necessarily uniform. In order to approach the performance of networks with uniformly distributed messages presented in theoretical studies, twin neurons are introduced. To assess the adapted model, twin neurons are used with real-world data to optimize power consumption of electronic circuits in practical test-cases