Invention Application
- Patent Title: COMPRESSED MATRIX REPRESENTATIONS OF NEURAL NETWORK ARCHITECTURES BASED ON SYNAPTIC CONNECTIVITY
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Application No.: US17364444Application Date: 2021-06-30
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Publication No.: US20230004791A1Publication Date: 2023-01-05
- Inventor: Yu Feng , Lam Thanh Nguyen
- Applicant: X Development LLC
- Applicant Address: US CA Mountain View
- Assignee: X Development LLC
- Current Assignee: X Development LLC
- Current Assignee Address: US CA Mountain View
- Main IPC: G06N3/08
- IPC: G06N3/08 ; G06N3/04

Abstract:
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for implementing brain emulation neural networks using compressed matrix representations. One of the methods includes obtaining a network input; and processing the network input using a neural network to generate a network output, comprising: processing the network input using an input subnetwork of the neural network to generate an embedding of the network input; and processing the embedding of the network input using a brain emulation subnetwork of the neural network, wherein the brain emulation subnetwork has a brain emulation neural network architecture that represents synaptic connectivity between a plurality of biological neurons in a brain of a biological organism, the processing comprising: obtaining a compressed matrix representation of a sparse matrix of brain emulation parameters; and applying the compressed matrix representation to the embedding of the network input to generate a brain emulation subnetwork output.
Information query