Loss-aware replication of neural network layers

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United States of America Patent

PATENT NO 11847567
SERIAL NO

16923003

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Some embodiments provide a method that receives a network with trained floating-point weight values. The network includes layers of nodes, each of which computes an output value based on input values and trained weight values. To replace a first layer of the trained network in a modified network with quantized weight values, the method defines multiple replica layers. Each replica layer includes nodes that correspond to nodes of the first layer, has a different set of allowed quantized weight values, and receives the same input values from a previous layer of the modified network such that groups of corresponding nodes from the replica layers operate correspondingly to the first layer. The method trains the quantized weight values of the modified network using a loss function with terms that account for effect on the loss function due to the quantization and for interactions between corresponding weight values of the replica layers.

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Patent Owner(s)

  • PERCEIVE CORPORATION

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Inventor(s)

Inventor Name Address # of filed Patents Total Citations
Drimbarean, Alexandru F Galway, IE 5 85
Sather, Eric A Palo Alto, US 23 59
Teig, Steven L Menlo Park, US 104 1160

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