According to some embodiments, the present disclosure may relate to a method of neural network analysis that includes receiving a first electronic message, storing it in a storage device, and decoding it to output a first data structure. The first electronic message may reference a first dictionary entry correlating the first electronic message to the first data structure including more bits than the first message. The method may also include providing the first data structure to a processing element to perform a data structure analysis on the first data structure yielding a second data structure including more bits than the first electronic message. The method may also include matching the second data structure to a second dictionary entry correlating the second data structure to a second electronic message that includes fewer bits than the second data structure, and transmitting the second electronic message instead of the second data structure.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of neural network analysis, the method comprising: receiving a first electronic message and storing the first electronic message in a storage device; decoding the first electronic message to output a first data structure, the first electronic message referencing a first dictionary entry that correlates the first electronic message to the first data structure, the first data structure including more bits than the first electronic message; providing the first data structure to a processing element to perform a data structure analysis in the neural network on the first data structure, the data structure analysis yielding a second data structure including more bits than the first electronic message; matching the second data structure to a second dictionary entry that correlates the second data structure to a second electronic message, the second electronic message including fewer bits than the second data structure; transmitting the second electronic message instead of the second data structure; and testing performance of a second dictionary that includes the second dictionary entry; and based on the test of the second dictionary, adjusting the size of the second dictionary. 2. The method of claim 1, further comprising: determining potential results for the data structure analysis; setting a size of the second dictionary to a number of dictionary entries; and clustering the potential results for the data structure analysis to determine multiple different entries for the second dictionary, a number of the different entries determined corresponding to the size of the second dictionary. 3. The method of claim 2, further comprising, based on the testing, storing the second dictionary in communication with the processing element and transmitting the second dictionary to another neural network layer component. 4. The method of claim 2, wherein the clustering is performed using one of a k-means algorithm or a principal component analysis whitening algorithm. 5. The method of claim 1, wherein the first electronic message is received from a previous layer of the neural network, the previous layer including a first dictionary with the first dictionary entry and the previous layer generates the first electronic message based on the first dictionary entry matching the first data structure. 6. The method of claim 1, wherein the first electronic message includes fewer than thirty-two bits and the first data structure includes a thirty-two bit floating point value. 7. The method of claim 1, further comprising: receiving the second electronic message corresponding to the second dictionary entry that correlates the second electronic message to the second data structure; and in response to decoding the second electronic message, retrieving the second data structure; and outputting the second data structure. 8. The method of claim 1, wherein a first dictionary includes the first dictionary entry and less than thirty-two additional dictionary entries. 9. The method of claim 1, wherein the method is performed on a mobile device. 10. A neural network computing system comprising: a first neural network layer including: a first storage device storing a first dictionary entry correlating a first data structure with a first electronic message; and a first transmission component configured to transmit the first electronic message, the first electronic message including fewer bits than the first data structure; a second neural network layer including: a second storage device configured to store the first electronic message received from the first neural network layer; a second processing element configured to: read the first electronic message and output the first data structure based on the first dictionary entry; perform a data structure analysis on the first data structure to yield a second data structure; match the second data structure to a second dictionary entry that correlates the second data structure to a second electronic message, the second electronic message including fewer bits than the second data structure; test performance of a second dictionary that includes the second dictionary entry; and based on the test of the second dictionary, adjust the size of the second dictionary; and a second transmission component configured to transmit the second electronic message. 11. The neural network computing system of claim 10, wherein the first dictionary entry is part of a first dictionary and the second dictionary entry is part of the second dictionary that is different from the first dictionary. 12. The neural network computing system of claim 10, wherein the first electronic message includes fewer than thirty-two bits and the first data structure includes a thirty-two bit floating point value. 13. The neural network computing system of claim 10, wherein a first dictionary includes the first dictionary entry and less than thirty-two additional dictionary entries and is included in both the first and second neural network layers. 14. The neural network computing system of claim 10, wherein the first storage device and the second storage devices are different regions of a single physical device, and the first neural network layer and the second neural network layer are logical layers of the single physical device. 15. The neural network computing system of claim 10, wherein the first neural network layer and the second neural network layer are on different physical devices communicating over a network. 16. A method of preparing a neural network, the method comprising: determining potential results of a data structure analysis associated with a layer of a neural network; setting a size of a dictionary to a number of dictionary entries for the layer; clustering the potential results of the data structure analysis to determine data structures and an associated electronic message for each of the number of dictionary entries, the number of dictionary entries corresponding to the size of the dictionary; testing the performance of the dictionary associated with the layer of the neural network; and based on the testing, storing the dictionary in adjacent layers in the neural network such that each layer in the neural network has a receiving dictionary that is correlated to a previous neural network layer transmitting dictionary, and a transmitting dictionary that is correlated to a following neural network layer receiving dictionary. 17. The method of claim 16, further comprising: based on the testing, modifying the size of the dictionary; and performing additional testing of the performance of the dictionary with the modified size. 18. The method of claim 16, wherein the size of the dictionary has a value of between zero and thirty-two. 19. The method of claim 16, wherein, for each layer, the receiving dictionary and the transmitting dictionary are different. 20. The method of claim 16, further comprising: receiving data for analysis in the neural network; and rather than transmitting the data, looking up a dictionary entry with a corresponding electronic message and transmitting the electronic message.