An apparatus for deciding a simplification policy for a neural network is provided. The deciding apparatus has a plurality of artificial neurons, a receiving circuit, a memory, and a simplifying module. The plurality of artificial neurons are configured to form an original neural network. The receiving circuit receives a set of sample for training the original neural network. The memory is used for recording a plurality of learnable parameters for the original neural network. After the original neural network has been trained with the set of sample, the simplifying module abandons a part of neuron connections in the original neural network based on the learnable parameters recorded by the memory. The simplifying module accordingly decides the structure of a simplified neural network and a plurality of learnable parameters for the simplified neural network.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A simplifying apparatus for a neural network, comprising: a plurality of artificial neurons configured to form an original neural network; a receiving circuit, coupled to the plurality of neurons, for receiving a set of sample for training the original neural network; a memory, coupled to the plurality of neurons, for recording a plurality of learnable parameters of the original neural network; and a simplifying module coupled to the memory, after the original neural network has been trained with the set of sample, the simplifying module abandoning a part of neuron connections in the original neural network based on the plurality of learnable parameters recorded in the memory, the simplifying module accordingly deciding the structure of a simplified neural network. 2. The simplifying apparatus of claim 1, wherein the plurality of learnable parameters comprises a weight parameter, the simplifying module judges whether the absolute value of the weight parameter is lower than a threshold; if the judging result is positive, the simplifying module abandons the neuron connection corresponding to this weight parameter. 3. The simplifying apparatus of claim 1, wherein the original neural network comprises a first artificial neuron and a second artificial neuron; based on the plurality of learnable parameters, the simplifying module determines whether to merge the operation executed by the first artificial neuron into the operation executed by the second artificial neuron. 4. The simplifying apparatus of claim 1, wherein the original neural network comprises a first computational layer and a second computational layer; based on the plurality of learnable parameters, the simplifying module determines whether to merge the operation executed by the first computational layer into the operation executed by the second computational layer. 5. The simplifying apparatus of claim 1, further comprising: an input analyzer for receiving a set of original samples and performing a component analysis on the set of original samples, so as to extract at least one basic component of the set of original samples, the input analyzer providing the at least one basic component to the receiving circuit as the set of sample for training the original neural network. 6. The simplifying apparatus of claim 5, wherein the component analysis is a principle component analysis or an independent component analysis. 7. The simplifying apparatus of claim 5, wherein after the simplified neural network is formed, the set of original samples is used to train the simplified neural network, so as to modify the plurality of learnable parameters for the simplified neural network. 8. The simplifying apparatus of claim 1, wherein after deciding the structure of the simplified neural network, the simplifying module reconfigures the plurality of artificial neurons to form the simplified neural network. 9. The simplifying apparatus of claim 1, wherein the simplifying module provides the structure of the simplified neural network to another plurality of artificial neurons. 10. A method for simplifying a neural network, comprising: (a) training an original neural network formed by a plurality of artificial neurons with a set of sample, so as to decide a plurality of learnable parameters of the original neural network; and (b) based on the plurality of learnable parameters decided in step (a), abandoning a part of neuron connections in the original neural network, so as to decide the structure of a simplified neural network. 11. The method of claim 10, wherein the plurality of learnable parameters comprises a weight parameter, and step (b) comprises: judging whether the absolute value of the weight parameter is lower than a threshold; and if the judging result is positive, abandoning the neuron connection corresponding to this weight parameter. 12. The method of claim 10, wherein the original neural network comprises a first artificial neuron and a second artificial neuron, and step (b) comprises: based on the plurality of learnable parameters, determining whether to merge the operation executed by the first artificial neuron into the operation executed by the second artificial neuron; and abandoning one or more neuron connection of the first artificial neuron. 13. The method of claim 10, wherein the original neural network comprises a first computational layer and a second computational layer, and step (b) comprises: based on the plurality of learnable parameters, determining whether to merge the operation executed by the first computational layer into the operation executed by the second computational layer; and abandoning one or more neuron connection of the first computational layer. 14. The method of claim 10, further comprising: receiving a set of original samples; performing a component analysis on the set of original samples, so as to extract at least one basic component of the set of original samples; and taking the at least one basic component as the set of sample for training the original neural network. 15. The method of claim 14, wherein the component analysis is a principle component analysis or an independent component analysis. 16. The method of claim 14, further comprising: after the simplified neural network is formed, training the simplified neural network with the set of original samples and accordingly modifying a plurality of learnable parameters of the simplified neural network. 17. The method of claim 10, further comprising: after step (b), reconfiguring the plurality of artificial neurons to form the simplified neural network. 18. The method of claim 10, further comprising: after step (b), applying the structure of the simplified neural network to another plurality of artificial neurons. 19. A non-transitory computer-readable storage medium encoded with a computer program for simplifying a neural network, the computer program comprising instructions that when executed by one or more computers cause the one or more computers to perform operations comprising: (a) training an original neural network formed by a plurality of artificial neurons with a set of sample, so as to decide a plurality of learnable parameters of the original neural network; and (b) based on the plurality of learnable parameters decided in operation (a), abandoning a part of neuron connections in the original neural network, so as to decide the structure of a simplified neural network. 20. The non-transitory computer-readable storage medium of claim 19, wherein the plurality of learnable parameters comprises a weight parameter, and the abandoning operation comprises: judging whether the absolute value of the weight parameter is lower than a threshold; and if the judging result is positive, abandoning the neuron connection corresponding to this weight parameter. 21. The non-transitory computer-readable storage medium of claim 19, wherein the original neural network comprises a first artificial neuron and a second artificial neuron, and the abandoning operation comprises: based on the plurality of learnable parameters, determining whether to merge the operation executed by the first artificial neuron into the operation executed by the second artificial neuron; and abandoning one or more neuron connection of the first artificial neuron. 22. The non-transitory computer-readable storage medium of claim 19, wherein the original neural network comprises a first computational layer and a second computational layer, and the abandoning operation comprises: based on the plurality of learnable parameters, determining whether to merge the operation executed by the first computational layer into the operation executed by the second computational layer; and abandoning one or more neuron connection of the first computational layer. 23. The non-transitory computer-readable storage medium of claim 19, wherein when executed by the one or more computers, the instructions further cause the one or more computers to perform operations comprising: receiving a set of original samples; performing a component analysis on the set of original samples, so as to extract at least one basic component of the set of original samples; and taking the at least one basic component as the set of sample for training the original neural network. 24. The non-transitory computer-readable storage medium of claim 23, wherein the component analysis is a principle component analysis or an independent component analysis. 25. The non-transitory computer-readable storage medium of claim 23, wherein when executed by the one or more computers, the instructions further cause the one or more computers to perform operations comprising: after the simplified neural network is formed, training the simplified neural network with the set of original samples and accordingly modifying a plurality of learnable parameters of the simplified neural network. 26. The non-transitory computer-readable storage medium of claim 19, wherein when executed by the one or more computers, the instructions further cause the one or more computers to perform operations comprising: after operation (b), reconfiguring the plurality of artificial neurons to form the simplified neural network. 27. The non-transitory computer-readable storage medium of claim 19, wherein when executed by the one or more computers, the instructions further cause the one or more computers to perform operations comprising: providing the structure of the simplified neural network to another plurality of artificial neurons.