A computer implemented method of compressing a set of coefficients (300, 502) for subsequent use in a neural network, the set of coefficients comprising a number of coefficients that are non-zero, the set of coefficients being weights of the neural network, the method comprising: applying sparsity to a plurality of groups of the coefficients, each group comprising a predefined plurality of coefficients, wherein each group comprises one or more subsets of coefficients of the set of coefficients (300, 502), each group comprising n coefficients and each subset comprising m coefficients, where m is greater than 1 and n is an integer multiple of m, and wherein applying sparsity to a group of coefficients comprises setting each of the coefficients in that group to zero; and characterised in that the method further comprises: compressing the plurality of groups of coefficients to which sparsity has been applied according to a compression scheme aligned with the plurality of groups of coefficients to which sparsity has been applied by compressing the one or more subsets of coefficients comprised by each group of the plurality of groups of coefficients to which sparsity has been applied so as to represent each subset of coefficients of the one or more subsets of coefficients by an integer number of one or more compressed values, wherein the one or more subsets of coefficients to be compressed comprise exclusively zero coefficients, wherein compressing a subset of coefficients comprising exclusively zero coefficients comprises encoding each coefficient in that subset of coefficients using zero bits of data and generating header data indicating, using one bit of data, that zero bits of data are used to encode each of the coefficients in the subset of coefficients, and wherein fewer bits are used to encode each of the compressed subsets of coefficients than are used to encode each of the subsets of coefficients in their uncompressed form; and storing the compressed groups of coefficients to memory for subsequent use in a neural network. applying sparsity to a plurality of groups of the coefficients, each group comprising a predefined plurality of coefficients, wherein each group comprises one or more subsets of coefficients of the set of coefficients (300, 502), each group comprising n coefficients and each subset comprising m coefficients, where m is greater than 1 and n is an integer multiple of m, and wherein applying sparsity to a group of coefficients comprises setting each of the coefficients in that group to zero; and compressing the plurality of groups of coefficients to which sparsity has been applied according to a compression scheme aligned with the plurality of groups of coefficients to which sparsity has been applied by compressing the one or more subsets of coefficients comprised by each group of the plurality of groups of coefficients to which sparsity has been applied so as to represent each subset of coefficients of the one or more subsets of coefficients by an integer number of one or more compressed values, wherein the one or more subsets of coefficients to be compressed comprise exclusively zero coefficients, wherein compressing a subset of coefficients comprising exclusively zero coefficients comprises encoding each coefficient in that subset of coefficients using zero bits of data and generating header data indicating, using one bit of data, that zero bits of data are used to encode each of the coefficients in the subset of coefficients, and wherein fewer bits are used to encode each of the compressed subsets of coefficients than are used to encode each of the subsets of coefficients in their uncompressed form; and storing the compressed groups of coefficients to memory for subsequent use in a neural network.