US Patent 11188744 Spatially sparse convolutional neural networks for inking applications

A spatially sparse convolutional neural network (CNN) framework is introduced to that leverages high sparsity of input data to significantly reduce the computational cost of applications that employ CNNs (e.g., inking applications and others) by avoiding unnecessary floating point mathematical operations. The framework, which is compatible with parallelized operations, includes (1) a data structure for sparse tensors that both (a) reduces storage burden and (b) speeds computations; (2) a set of sparse tensor operations that accelerate convolution computations; and (3) the merging of pooling and convolutional layers. Practical applications involving handwriting recognition and/or stroke analysis demonstrate a notable reduction in storage and computational burdens.