Invention Grant
- Patent Title: Compression of fully connected / recurrent layers of deep network(s) through enforcing spatial locality to weight matrices and effecting frequency compression
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Application No.: US15827465Application Date: 2017-11-30
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Publication No.: US11977974B2Publication Date: 2024-05-07
- Inventor: Chia-Yu Chen , Jungwook Choi , Kailash Gopalakrishnan , Suyog Gupta , Pritish Narayanan
- Applicant: International Business Machines Corporation
- Applicant Address: US NY Armonk
- Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
- Current Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
- Current Assignee Address: US NY Armonk
- Agency: Amin, Turocy & Watson, LLP
- Main IPC: G06N3/08
- IPC: G06N3/08 ; G06F17/14

Abstract:
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.
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