Embodiments include computing devices, apparatus, and methods implemented by the apparatus for accelerating machine learning on a computing device. Raw data may be received in the computing device from a raw data source device. The apparatus may identify key features as two dimensional matrices of the raw data such that the key features are mutually exclusive from each other. The key features may be translated into key feature vectors. The computing device may generate a feature vector from at least one of the key feature vectors. The computing device may receive a first partial output resulting from an execution of a basic linear algebra subprogram (BLAS) operation using the feature vector and a weight factor. The first partial output may be combined with a plurality of partial outputs to produce an output matrix. Receiving the raw data on the computing device may include receiving streaming raw data.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of accelerating machine learning on a computing device, comprising: receiving raw data from a raw data source device; identifying key features as two dimensional matrices of the raw data such that the key features are mutually exclusive from each other; translating the key features into key feature vectors; generating a feature vector from at least one of the key feature vectors; receiving a first partial output resulting from an execution of a basic linear algebra subprogram (BLAS) operation using the feature vector and a weight factor; and combining the first partial output with a plurality of partial outputs to produce an output matrix. 2. The method of claim 1, wherein identifying key features as two dimensional matrices of the raw data such that the key features are mutually exclusive from each other comprises: identifying a first key feature as a first two dimensional matrix of a designated size; and identifying a second key feature as a second two dimensional matrix of the designated size a designated number of units from the first key feature. 3. The method of claim 1, wherein generating a feature vector from at least one of the key feature vectors comprises: selecting a top key feature vector from a key feature vector queue; and using the top key feature vector as the feature vector. 4. The method of claim 1, wherein generating a feature vector from at least one of the key feature vectors comprises: selecting a top key feature vector from a key feature vector queue; selecting a next key feature vector from the key feature vector queue; selecting top key feature vector positions and next key feature vector positions; and combining the selected top key feature vector position and the selected next key feature vector positions into the feature vector. 5. The method of claim 4, wherein: selecting top key feature vector positions and next key feature vector positions comprises selecting the top key feature vector positions and the next key feature vector positions such that each of the selected top key feature vector position and the selected next key feature vector positions represent mutually exclusive locations from each other in the raw data and represent an unidentified key feature of raw data that spans a plurality of the identified key features of the raw data; and combining the selected top key feature vector position and the selected next key feature vector positions into the feature vector comprises combining the selected top key feature vector position and the selected next key feature vector positions into the feature vector such that the feature vector is configured like a key feature vector of the unidentified key feature. 6. The method of claim 1, further comprising: activating a set of vector units upon receiving the raw data at a feature buffer associated with the set of vector units, wherein the set of vector units is mapped to the output matrix; executing the BLAS operation by each vector unit of the set of vector units; and outputting at least one partial output by each vector unit. 7. The method of claim 6, further comprising: determining whether any feature vectors remain for use in an execution of the BLAS operation by the set of vector units; and deactivating the set of vector units in response to determining that no feature vectors remain for use in an execution of the BLAS operation by the set of vector units. 8. The method of claim 1, wherein receiving raw data from a raw data source device comprises receiving streaming raw data from the raw data source device. 9. An apparatus configured to accelerate machine learning on a computing device, comprising: a raw data source device; and a vectorization unit communicatively connected to the raw data source device, and configured to perform operations comprising: receiving raw data from the raw data source device; identifying key features as two dimensional matrices of the raw data such that the key features are mutually exclusive from each other; translating the key features into key feature vectors; generating a feature vector from at least one of the key feature vectors; receiving a first partial output resulting from an execution of a basic linear algebra subprogram (BLAS) operation using the feature vector and a weight factor; and combining the first partial output with a plurality of partial outputs to produce an output matrix. 10. The apparatus of claim 9, wherein the vectorization unit is configured to perform operations such that identifying key features as two dimensional matrices of the raw data such that the key features are mutually exclusive from each other comprises: identifying a first key feature as a first two dimensional matrix of a designated size; and identifying a second key feature as a second two dimensional matrix of the designated size a designated number of units from the first key feature. 11. The apparatus of claim 9, wherein the vectorization unit is configured to perform operations such that generating a feature vector from at least one of the key feature vectors comprises: selecting a top key feature vector from a key feature vector queue; and using the top key feature vector as the feature vector. 12. The apparatus of claim 9, wherein the vectorization unit is configured to perform operations such that generating a feature vector from at least one of the key feature vectors comprises: selecting a top key feature vector from a key feature vector queue; selecting a next key feature vector from the key feature vector queue; selecting top key feature vector positions and next key feature vector positions; and combining the selected top key feature vector position and the selected next key feature vector positions into the feature vector. 13. The apparatus of claim 12, wherein the vectorization unit is configured to perform operations such that: selecting top key feature vector positions and next key feature vector positions comprises selecting the top key feature vector positions and the next key feature vector positions such that each of the selected top key feature vector position and the selected next key feature vector positions represent mutually exclusive locations from each other in the raw data and represent an unidentified key feature of raw data that spans a plurality of the identified key features of the raw data; and combining the selected top key feature vector position and the selected next key feature vector positions into the feature vector comprises combining the selected top key feature vector position and the selected next key feature vector positions into the feature vector such that the feature vector is configured like a key feature vector of the unidentified key feature. 14. The apparatus of claim 9, further comprising a set of vector units communicatively connected to the vectorization unit, wherein the set of vector units is mapped to the output matrix, and wherein: the vectorization unit comprises a feature buffer associated with the set of vector units, and the vectorization unit is configured to execute operations further comprising activating the set of vector units upon receiving the raw data at the feature buffer associated with the set of vector units; each vector unit of the set of vector units is configured to perform operations comprising: executing the BLAS operation; and outputting at least one partial output. 15. The apparatus of claim 14, wherein the vectorization unit is configured to execute operations further comprising: determining whether any feature vectors remain for use in an execution of the BLAS operation by the set of vector units; and deactivating the set of vector units in response to determining that no feature vectors remain for use in an execution of the BLAS operation by the set of vector units. 16. The apparatus of claim 9, wherein the vectorization unit is configured to execute operations such that receiving raw data from a raw data source device comprises receiving streaming raw data from the raw data source device. 17. An apparatus configured to accelerate machine learning on a computing device, comprising: means for receiving raw data from a raw data source device; means for identifying key features as two dimensional matrices of the raw data such that the key features are mutually exclusive from each other; means for translating the key features into key feature vectors; means for generating a feature vector from at least one of the key feature vectors; means for receiving a first partial output resulting from an execution of a basic linear algebra subprogram (BLAS) operation using the feature vector and a weight factor; and means for combining the first partial output with a plurality of partial outputs to produce an output matrix. 18. The apparatus of claim 17, wherein means for identifying key features as two dimensional matrices of the raw data such that the key features are mutually exclusive from each other comprises: means for identifying a first key feature as a first two dimensional matrix of a designated size; and means for identifying a second key feature as a second two dimensional matrix of the designated size a designated number of units from the first key feature. 19. The apparatus of claim 17, wherein means for generating a feature vector from at least one of the key feature vectors comprises: means for selecting a top key feature vector from a key feature vector queue; and means for using the top key feature vector as the feature vector. 20. The apparatus of claim 17, wherein means for generating a feature vector from at least one of the key feature vectors comprises: means for selecting a top key feature vector from a key feature vector queue; means for selecting a next key feature vector from the key feature vector queue; means for selecting top key feature vector positions and next key feature vector positions; and means for combining the selected top key feature vector position and the selected next key feature vector positions into the feature vector. 21. The apparatus of claim 20, wherein: means for selecting top key feature vector positions and next key feature vector positions comprises means for selecting the top key feature vector positions and the next key feature vector positions such that each of the selected top key feature vector position and the selected next key feature vector positions represent mutually exclusive locations from each other in the raw data and represent an unidentified key feature of raw data that spans a plurality of the identified key features of the raw data; and means for combining the selected top key feature vector position and the selected next key feature vector positions into the feature vector comprises means for combining the selected top key feature vector position and the selected next key feature vector positions into the feature vector such that the feature vector is configured like a key feature vector of the unidentified key feature. 22. The apparatus of claim 17, further comprising: means for executing the BLAS operation; means for outputting at least one partial output, wherein means for executing the BLAS operation and means for outputting at least one partial output are mapped to the output matrix; means for activating means for executing the BLAS operation and means for outputting the at least one partial output upon receiving the raw data; means for determining whether any feature vectors remain for use in an execution of the BLAS operation; and means for deactivating means for executing the BLAS operation and means for outputting the at least one partial output in response to determining that no feature vectors remain for use in an execution of the BLAS operation. 23. The apparatus of claim 17, wherein means for receiving raw data from a raw data source device comprises means for receiving streaming raw data from the raw data source device. 24. A non-transitory processor-readable storage medium having stored thereon processor-executable instructions configured to cause a processor of a computing device to perform operations comprising: receiving raw data from a raw data source device; identifying key features as two dimensional matrices of the raw data such that the key features are mutually exclusive from each other; translating the key features into key feature vectors; generating a feature vector from at least one of the key feature vectors; receiving a first partial output resulting from an execution of a basic linear algebra subprogram (BLAS) operation using the feature vector and a weight factor; and combining the first partial output with a plurality of partial outputs to produce an output matrix. 25. The non-transitory processor-readable storage medium of claim 24, wherein the stored processor-executable instructions are configured to cause the processor to perform operations such that identifying key features as two dimensional matrices of the raw data such that the key features are mutually exclusive from each other comprises: identifying a first key feature as a first two dimensional matrix of a designated size; and identifying a second key feature as a second two dimensional matrix of the designated size a designated number of units from the first key feature. 26. The non-transitory processor-readable storage medium of claim 24, wherein the stored processor-executable instructions are configured to cause the processor to perform operations such that generating a feature vector from at least one of the key feature vectors comprises: selecting a top key feature vector from a key feature vector queue; and using the top key feature vector as the feature vector. 27. The non-transitory processor-readable storage medium of claim 24, wherein the stored processor-executable instructions are configured to cause the processor to perform operations such that generating a feature vector from at least one of the key feature vectors comprises: selecting a top key feature vector from a key feature vector queue; selecting a next key feature vector from the key feature vector queue; selecting top key feature vector positions and next key feature vector positions; and combining the selected top key feature vector position and the selected next key feature vector positions into the feature vector. 28. The non-transitory processor-readable storage medium of claim 27, wherein the stored processor-executable instructions are configured to cause the processor to perform operations such that: selecting top key feature vector positions and next key feature vector positions comprises selecting the top key feature vector positions and the next key feature vector positions such that each of the selected top key feature vector position and the selected next key feature vector positions represent mutually exclusive locations from each other in the raw data and represent an unidentified key feature of raw data that spans a plurality of the identified key features of the raw data; and combining the selected top key feature vector position and the selected next key feature vector positions into the feature vector comprises combining the selected top key feature vector position and the selected next key feature vector positions into the feature vector such that the feature vector is configured like a key feature vector of the unidentified key feature. 29. The non-transitory processor-readable storage medium of claim 24, wherein the stored processor-executable instructions are configured to cause the processor to perform operations further comprising: activating the processor upon receiving the raw data, wherein the processor is mapped to the output matrix; executing the BLAS operation; outputting at least one partial output; determining whether any feature vectors remain for use in an execution of the BLAS operation by the processor; and deactivating the processor in response to determining that no feature vectors remain for use in an execution of the BLAS operation by the processor. 30. The non-transitory processor-readable storage medium of claim 24, wherein the stored processor-executable instructions are configured to cause the processor to perform operations such that receiving raw data from a raw data source device comprises receiving streaming raw data from the raw data source device.