Sparse binary matrices as efficient associative memories

Vincent Gripon, Vitaly Skachek, Michael G. Rabbat

Abstract: Associative memories are widely used devices which can be viewed as universal error-correcting decoders. Employing error-correcting code principles in these devices has allowed to greatly enhance their performance. In this paper we reintroduce a neural-based model using the formalism of linear algebra and extend its functionality, originally limited to erasure retrieval, to handle approximate inputs. In order to perform the retrieval, we use an iterative algorithm that provably converges. We then analyze the performance of the associative memory under the assumption of connection independence. We support our theoretical results with numerical simulations.