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AssigneeInternational Business Machines Corporation
InventorsChia-Yu Chen, Jungwook Choi, Kailash Gopalakrishnan, Suyog Gupta, Pritish Narayanan
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A system, having a memory that stores computer executable components, and a processor that executes the computer executable components, reduces data size in connection with training a neural network by exploiting spatial locality to weight matrices and effecting frequency transformation and compression. A receiving component receives neural network data in the form of a compressed frequency-domain weight matrix. A segmentation component segments the initial weight matrix into original sub-components, wherein respective original sub-components have spatial weights. A sampling component applies a generalized weight distribution to the respective original sub-components to generate respective normalized sub-components. A transform component applies a transform to the respective normalized sub-components. A cropping component crops high-frequency weights of the respective transformed normalized sub-components to yield a set of low-frequency normalized sub-components to generate a compressed representation of the original sub-components.
A system for compressing data during neural network training, comprising:
A computer-implemented method, comprising employing a processor and memory to execute computer executable components to perform the following acts:
A computer program product for compressing training data, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by processor to cause the processor to:
Dependencies are inferred from the claim text.