The disclosed system incorporates a new learning module, the Learning Kernel Activation Module (LKAM), at least serving the purpose of enforcing the utilization of less convolutional kernels by learning kernel activation rules and by actually controlling the engagement of various computing elements: The exemplary module activates/deactivates a sub-set of filtering kernels, groups of kernels, or groups of full connected neurons, during the inference phase, on-the-fly for every input image depending on the input image content and the learned activation rules.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of building and training a deep convolutional neural networks (CNN), comprising: utilizing a learning kernel-activation module (LKAM) which is inserted between a first and a second convolutional layer in a deep CNN, wherein said LKAM is a small CNN having as inputs the features that are output from the first convolutional layer and has as output a vector of numbers indicating whether the convolutional kernels in the second convolutional layer are on or off. 2. The method of claim 1, wherein the level of engagement is determined through the optimization of a cost function that utilizes a regularization term proportional to the number of kernels that are engaged in each forward propagation step. 3. The method of claim 2, wherein the cost function is L aug = G i 2  m  ∑ i   sw i  , where swi are the elements of SW vector which is the output of LKAM, Gi is a gain factor and m is the length of the SW vector. 4. The method of claim 1, where the convolutional kernels that are off are electrically switched-off when said deep neural networks are implemented in VLSI. 5. The method of claim 1, wherein the number of convolutional kernels to be computed as on or off is a variable. 6. A non-transitory computer readable information storage media having stored therein instructions, that when executed by one or more processors, cause to be performed a method of building and training a deep convolutional neural networks (CNN) comprising: utilizing a learning kernel-activation module (LKAM) which is inserted between a first and a second convolutional layer in a deep CNN, wherein said LKAM is a small CNN having as inputs the features that are output from the first convolutional layer and has as output a vector of numbers indicating whether the convolutional kernels in the second convolutional layer are on or off. 7. The media of claim 6, wherein the level of engagement is determined through the optimization of a cost function that utilizes a regularization term proportional to the number of kernels that are engaged in each forward propagation step. 8. The media of claim 7, wherein the cost function is L aug = G i 2  m  ∑ i   sw i  , where swi are the elements of SW vector which is the output of LKAM, Gi is a gain factor and m is the length of the SW vector. 9. The media of claim 6, where the convolutional kernels that are off are electrically switched-off when said deep neural networks are implemented in VLSI. 10. The media of claim 6, wherein the number of convolutional kernels to be computed as on or off is a variable. 11. A system that builds and trains a deep convolutional neural network (CNN) comprising: means, including a processor and memory, for utilizing a learning kernel-activation module (LKAM) which is inserted between a first and a second convolutional layer in a deep CNN, wherein said LKAM is a small CNN having as inputs the features that are output from the first convolutional layer and has as output a vector of numbers indicating whether the convolutional kernels in the second convolutional layer are on or off.