Described is a sparse inference module that can be incorporated into a deep learning system. For example, the deep learning system includes a plurality of hierarchical feature channel layers, each feature channel layer having a set of filters. A plurality of sparse inference modules can be included such that a sparse inference module resides electronically within each feature channel layer. Each sparse inference module is configured to receive data and match the data against a plurality of pattern templates to generate a degree of match value for each of the pattern templates, with the degree of match values being sparsified such that only those degree of match values that exceed a predetermined threshold, or a fixed number of the top degree of match values, are provided to subsequent feature channels in the plurality of hierarchical feature channels, while other, losing degree of match values are quenched to zero.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A sparse inference module for deep learning, the sparse inference module comprising: one or more processors and a memory, the memory have executable instructions encoded thereon, such that upon execution, the one or more processors perform operations of: receiving data and matching the data against a plurality of pattern templates to generate a degree of match value for each of the pattern templates; sparsifying the degree of match values such that only those degree of match values that satisfy a criterion are provided for further processing as sparse feature vectors, while other losing degree of match values are quenched to zero; and using the sparse feature vectors to self-select a channel that participates in high-level classification. 2. The sparse inference module for deep learning of claim 1, wherein the data comprises at least one of still image information, video information, and audio information, 3. The sparse inference module for deep learning of claim 1, wherein self-selection of the channel facilitates classification of at least one of still image information, video information, and audio information. 4. The sparse inference module for deep learning of claim 1, wherein the criterion requires the degree of match value to be above a threshold limit. 5. The sparse inference module for deep learning of claim 1, wherein the criterion requires the degree of match value to be within a fixed quantity of the top degree of match values. 6. A computer program product for sparse inference for deep learning, the computer program product comprising: a non-transitory computer-readable medium having executable instructions encoded thereon, such that upon execution of the instructions by one or more processors, the one or more processors perform operations of: receiving data and matching the data against a plurality of pattern templates to generate a degree of match value for each of the pattern templates; sparsifying the degree of match values such that only those degree of match values that satisfy a criterion are provided for further processing as sparse feature vectors, while other losing degree of match values are quenched to zero; and using the sparse feature vectors to self-select a channel that participates in high-level classification. 7. The computer program product of claim 6, wherein the data comprises at least one of still image information, video information, and audio information. 8. The computer program product of claim 6, wherein self-selection of the channel facilitates classification of at least one of still image information, video information, and audio information. 9. The computer program product of claim 6, wherein the criterion requires the degree of match value to he above a threshold limit. 10. The computer program product of claim 6, wherein the criterion requires the degree of match value to be within a fixed quantity of the top degree of match values. 11. A method for sparse inference for deep learning, the method comprising an act of: causing one or more processers to execute instructions encoded on a non-transitory computer-readable medium, such that upon execution, the one or more processors perform operations of: receiving data and matching the data against a plurality of pattern templates to generate a degree of match value for each of the pattern templates; sparsifying the degree of match values such that only those degree of match values that satisfy a criterion are provided for further processing as sparse feature vectors, while other losing degree of match values are quenched to zero; and using the sparse feature vectors to self-select a channel that participates in high-level classification. 12. The, method of claim 11, wherein the data comprises at least one of still image information, video information, and audio information. 13. The method of claim 11, wherein self-selection of the channel facilitates classification of at least one of still image information, video information, and audio information. 14. The method of claim 11, wherein the criterion requires the degree of match value to be above a threshold limit. 15. The method of claim 11, wherein the criterion requires the degree of match value to be within a fixed quantity of the top degree of match values. 16. A deep learning system using sparse learning modules, the deep learning system comprising: a plurality of hierarchical feature channel layers, each feature channel layer having a set of filters that filter data received in the feature channel; a plurality of sparse inference modules, where a sparse inference module resides electronically within each feature channel layer; and wherein one or more of the sparse inference module is configured receive data and match the data against a plurality of pattern templates to generate a degree of match value for each of the pattern templates, and sparsify the degree of match values such that only those degree of match values that satisfy a criterion are provided for further processing as sparse feature vectors, while other losing degree of match values are quenched to zero, and use the sparse feature vectors to self-select a channel that participates in high-level classification. 17. The deep learning system as set forth in claim 16, wherein the deep learning system is a convolution neural network (CNN) and the plurality of hierarchical feature channel layers include a first matching layer and a second matching layer, and further comprising: a first pooling layer electronically positioned between the first and second matching layers; and a second pooling layer, the second pooling layer positioned downstream from the second matching layer. 18. The deep learning system as set forth in claim 17, wherein the first feature matching layer includes a set of filters, a compressive nonlinearity module, and a sparse inference module. 19. The deep learning system as set forth in claim 17, wherein the second feature matching layer includes a set of filters, a compressive nonlinearity module, and a sparse inference module. 20. The deep learning system as set forth in claim 17, wherein the first pooling layer includes a pooling module and, a sparse inference module. 21. The deep learning system as set forth in claim 17, wherein the second pooling layer includes a pooling module and a sparse inference module. 22. The deep learning system as set forth in claim 16, wherein the sparse learning modules further operate across spatial locations in each of the feature channel layers.