A computer-implemented method for representation of weight values in an artificial neural network using inter-group indexing may include, in an artificial neural network that includes neurons and synaptic connections between the neurons with each of the synaptic connections including a weight, arranging the weights in ascending order. The method may also include dividing the arranged weights into groups based on approximately linear patterns of the weights. The method may further include designating a base group and the other groups as dependent groups. The method may also include storing in memory values of the weights in the base group. The method may further include storing in the memory a group index for each of the dependent groups. The method may also include storing in the memory an index for each of the weights in the dependent groups corresponding to one of the weights in the base group without storing in the memory values of the weights in the dependent groups.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method for representation of weight values in an artificial neural network using inter-group indexing, the method comprising: in an artificial neural network that includes neurons and synaptic connections between the neurons with each of the synaptic connections including a weight, arranging the weights in ascending order; dividing the arranged weights into groups based on approximately linear patterns of the weights; designating a base group and the other groups as dependent groups; storing in memory values of the weights in the base group; storing in the memory a group index for each of the dependent groups; and storing in the memory an index for each of the weights in the dependent groups corresponding to one of the weights in the base group without storing in the memory values of the weights in the dependent groups. 2. The computer-implemented method of claim 1, wherein the designating of the base group includes designating the group having the approximately linear pattern that is the most linear as the base group. 3. The computer-implemented method of claim 1, wherein the designating of the base group includes designating a virtual group as the base group. 4. The computer-implemented method of claim 1, wherein the storing in the memory of the values of the weights in the base group includes storing in the memory the values of the weights in the base group as floating point numbers. 5. The computer-implemented method of claim 4, wherein the storing in the memory of the index for each of the weights in the dependent groups corresponding to one of the weights in the base group without storing in the memory the values of the weights in the dependent groups includes storing in the memory the index as an integer number for each of the weights in the dependent groups corresponding to one of the weights in the base group without storing in the memory the values of the weights in the dependent groups as floating point numbers. 6. The computer-implemented method of claim 1, further comprising: calculating slope and intercept modifiers for each of the dependent groups; and storing in the memory the slope and intercept modifiers for each of the dependent groups. 7. The computer-implemented method of claim 6, further comprising: retrieving from the memory the slope and intercept modifiers for one of the dependent groups; retrieving from the memory the index of one of the weights in the dependent group; retrieving from the memory the value of the weight in the base group to which the retrieved index corresponds; and synthesizing the weight in the dependent group using the retrieved index of the weight, the retrieved slope and intercept modifiers of the dependent group, and the retrieved value of the weight in the base group. 8. A non-transitory computer-readable storage media including computer-executable instructions configured to cause a system to perform operations for representation of weight values in an artificial neural network using inter-group indexing, the operations comprising: in an artificial neural network that includes neurons and synaptic connections between the neurons with each of the synaptic connections including a weight, arranging the weights in ascending order; dividing the arranged weights into groups based on approximately linear patterns of the weights; designating a base group and the other groups as dependent groups; storing in memory values of the weights in the base group; storing in the memory a group index for each of the dependent groups; and storing in the memory an index for each of the weights in the dependent groups corresponding to one of the weights in the base group without storing in the memory values of the weights in the dependent groups. 9. The non-transitory computer-readable storage media of claim 8, wherein the designating of the base group includes designating the group having the approximately linear pattern that is the most linear as the base group. 10. The non-transitory computer-readable storage media of claim 8, wherein the designating of the base group includes designating a virtual group as the base group. 11. The non-transitory computer-readable storage media of claim 8, wherein the storing in the memory of the values of the weights in the base group includes storing in the memory the values of the weights in the base group as floating point numbers. 12. The non-transitory computer-readable storage media of claim 11, wherein the storing in the memory of the index for each of the weights in the dependent groups corresponding to one of the weights in the base group without storing in the memory the values of the weights in the dependent groups includes storing in the memory the index as an integer number for each of the weights in the dependent groups corresponding to one of the weights in the base group without storing in the memory the values of the weights in the dependent groups as floating point numbers. 13. The non-transitory computer-readable storage media of claim 8, further comprising: calculating slope and intercept modifiers for each of the dependent groups; and storing in the memory the slope and intercept modifiers for each of the dependent groups. 14. The non-transitory computer-readable storage media of claim 13, further comprising: retrieving from the memory the slope and intercept modifiers for one of the dependent groups; retrieving from the memory the index of one of the weights in the dependent group; retrieving from the memory the value of the weight in the base group to which the retrieved index corresponds; and synthesizing the weight in the dependent group using the retrieved index of the weight, the retrieved slope and intercept modifiers of the dependent group, and the retrieved value of the weight in the base group. 15. A computer architecture for representation of weight values in an artificial neural network using inter-group indexing, the computer architecture comprising: memory; and a processing element configured to: access an artificial neural network that includes neurons and synaptic connections between the neurons with each of the synaptic connections including a weight; arrange the weights in ascending order; divide the arranged weights into groups based on approximately linear patterns of the weights; designate a base group and the other groups as dependent groups; store in the memory values of the weights in the base group; store in the memory a group index for each of the dependent groups; calculate slope and intercept modifiers for each of the dependent groups; store in the memory the slope and intercept modifiers for each of the dependent groups; and store in the memory an index for each of the weights in the dependent groups corresponding to one of the weights in the base group without storing in the memory values of the weights in the dependent groups. 16. The computer architecture of claim 15, wherein the designating of the base group includes designating the group having the approximately linear pattern that is the most linear as the base group. 17. The computer architecture of claim 15, wherein the designating of the base group includes designating a virtual group as the base group. 18. The computer architecture of claim 15, wherein the storing in the memory of the values of the weights in the base group includes storing in the memory the values of the weights in the base group as floating point numbers. 19. The computer architecture of claim 18, wherein the storing in the memory of the index for each of the weights in the dependent groups corresponding to one of the weights in the base group without storing in the memory the values of the weights in the dependent groups includes storing in the memory the index as an integer number for each of the weights in the dependent groups corresponding to one of the weights in the base group without storing in the memory the values of the weights in the dependent groups as floating point numbers. 20. The computer architecture of claim 15, further comprising: a weight modifier configured to: retrieve from the memory the slope and intercept modifiers for one of the dependent groups; retrieve from the memory the index of one of the weights in the dependent group; retrieve from the memory the value of the weight in the base group to which the retrieved index corresponds; and synthesize the weight in the dependent group using the retrieved index of the weight, the retrieved slope and intercept modifiers of the dependent group, and the retrieved value of the weight in the base group.