A parallel convolutional neural network is provided. The CNN is implemented by a plurality of convolutional neural networks each on a respective processing node. Each CNN has a plurality of layers. A subset of the layers are interconnected between processing nodes such that activations are fed forward across nodes. The remaining subset is not so interconnected.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A convolutional neural network system comprising: a sequence of neural network layers, wherein the sequence of neural network layers comprises: a first convolutional layer configured to receive a first convolutional layer input derived from an input image and to process the first convolutional layer input to generate a first convolved output; a first max-pooling layer immediately after the first convolutional layer in the sequence configured to pool the first convolved output to generate a first pooled output; a second convolutional layer immediately after the max-pooling layer in the sequence configured to receive the first pooled output and to process the first pooled output to generate a second convolved output, and a plurality of fully-connected layers after the second convolutional layer in the sequence configured to receive an output derived from the second convolved output and to collectively process the output derived from the second convolved output to generate a sequence output for the input image. 2. The convolutional neural network system of claim 1, wherein the plurality of fully-connected layers are not immediately after the second convolutional layer in the sequence. 3. The convolutional neural network system of claim 1, wherein the second convolutional layer is not immediately followed by a max-pooling layer in the sequence. 4. The convolutional neural network system of claim 1, wherein the sequence of neural network layers comprises one or more other layers between the second convolutional layers and the plurality of fully-connected layers. 5. The convolutional neural network system of claim 1, wherein the sequence of neural network layers comprises one or more non max-pooling layers immediately after the second convolutional layer in the sequence. 6. The convolutional neural network system of claim 1, further comprising: one or more other sequences of neural network layers, each other sequence comprising layers identical to the first convolutional layer, the first max-pooling layer, the second convolutional layer, and the plurality of fully-connected layers. 7. The convolutional neural network system of claim 6, wherein at least one of the plurality of fully-connected layers is configured to provide an output to a layer in the at least one other sequence of neural network layers. 8. The convolutional neural network system of claim 6, wherein the second convolutional layer is configured to provide the second convolved output only to another layer in the sequence of neural network layers and not to any layers in any of the other sequences of neural network layers. 9. The convolutional neural network system of claim 6, wherein each other sequence of neural network layers is configured to collectively process the input image to generate a respective sequence output for the input image. 10. The convolutional neural network system of claim 9, further comprising: an output layer configured to receive the sequence outputs for each sequence of layers and to classify the input image by processing the sequence outputs. 11. A method for processing an input image using a convolutional neural network system comprising a sequence of neural network layers, the method comprising: processing a first convolutional layer input derived from the input image using a first convolutional layer in the sequence, wherein the first convolutional layer is configured to receive the first convolutional layer input and to process the first convolutional layer input to generate a first convolved output; processing the first convolved output using a first max-pooling layer immediately after the first convolutional layer in the sequence, wherein the first max-pooling layer is configured to pool the first convolved output to generate a first pooled output; processing the first pooled output using a second convolutional layer immediately after the max-pooling layer in the sequence, wherein the second convolutional layer is configured to receive the first pooled output and to process the first pooled output to generate a second convolved output; and processing an output derived from the second convolved output using a plurality of fully-connected layers after the second convolutional layer in the sequence, wherein the plurality of fully-connected layers are configured to receive the output derived from the second convolved output and to collectively process the output derived from the second convolved output to generate a sequence output for the input image. 12. The method of claim 11, wherein the plurality of fully-connected layers are not immediately after the second convolutional layer in the sequence. 13. The method of claim 11, wherein the second convolutional layer is not immediately followed by a max-pooling layer in the sequence. 14. The method of claim 11, wherein the sequence of neural network layers comprises one or more other layers between the second convolutional layers and the plurality of fully-connected layers. 15. The method of claim 11, wherein the sequence of neural network layers comprises one or more non max-pooling layers immediately after the second convolutional layer in the sequence. 16. The method of claim 11, wherein the neural network system further comprises one or more other sequences of neural network layers, each other sequence comprising layers identical to the first convolutional layer, the first max-pooling layer, the second convolutional layer, and the plurality of fully-connected layers. 17. The method of claim 16, wherein at least one of the plurality of fully-connected layers is configured to provide an output to a layer in the at least one other sequence of neural network layers. 18. The method of claim 16, wherein the second convolutional layer is configured to provide the second convolved output only to another layer in the sequence of neural network layers and not to any layers in any of the other sequences of neural network layers. 19. The method of claim 16, further comprising processing the input image using each other sequence of neural network layers, wherein each other sequence of neural network layers is configured to collectively process the input image to generate a respective sequence output for the input image. 20. A computer program product encoded on one or more non-transitory computer readable media, the computer program product comprising instructions that when executed by one or more computers cause the one or more computers to perform operations for processing an input image using a convolutional neural network system comprising a sequence of neural network layers, the operations comprising: processing a first convolutional layer input derived from the input image using a first convolutional layer in the sequence, wherein the first convolutional layer is configured to receive the first convolutional layer input and to process the first convolutional layer input to generate a first convolved output; processing the first convolved output using a first max-pooling layer immediately after the first convolutional layer in the sequence, wherein the first max-pooling layer is configured to pool the first convolved output to generate a first pooled output; processing the first pooled output using a second convolutional layer immediately after the max-pooling layer in the sequence, wherein the second convolutional layer is configured to receive the first pooled output and to process the first pooled output to generate a second convolved output; and processing an output derived from the second convolved output using a plurality of fully-connected layers after the second convolutional layer in the sequence, wherein the plurality of fully-connected layers are configured to receive the output derived from the second convolved output and to collectively process the output derived from the second convolved output to generate a sequence output for the input image.