Artificial neural network implementation in field-programmable gate arrays

Field-programmable gate array and method to implement an artificial neural network. A trained model of the neural network is processed, in which weights are defined in a floating-point format, to quantize each set of weights to a respective reduced-precision format in dependence on effect of quantization on accuracy of the model. For each set of weights, a partitioning scheme is defined for a set of block memories of the apparatus such that a plurality k of those weights can be stored in each addressable location of the set of memories, wherein k differs for different sets of weights. The apparatus can be programmed to implement the neural network such that weights in each set are persistently stored in a set of block memories partitioned according to the partitioning scheme for that set of weights.