Abstract: Generally discussed herein are devices, systems, and methods for machine-learning. A method may include training, based on sparseness constraints and using a first device, a sparse matrix, prototype vectors, prototype labels, and corresponding prototype score vectors, simultaneously, storing the sparse matrix, prototype vectors, and prototype labels on a random-access memory (RAM) of a second device, projecting, using the second device, a prediction vector of a second dimensional space to the first dimensional space, the first dimensional space less than the second dimensional space, determining whether the projected prediction vector is closer to the one or more first prototype vectors or the one or more second prototype vectors, and determining a prediction by identifying the which prediction outcome the projected prediction vector is closer to.