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US 12,639,559 B2Granted
Calibration of electrical parameters in a deep learning artificial neural network
AssigneeSilicon Storage Technology, Inc.
InventorHieu Van Tran
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Abstract
Numerous examples are disclosed for performing calibration of various electrical parameters in a deep learning artificial neural network. In one example, a method comprises adjusting a bias voltage applied to one or more non-volatile memory cells in an artificial neural network, performing a performance target check on the one or more non-volatile memory cells in the artificial neural network, and repeating the adjusting and performing until the performance target check indicates an electrical parameter is within a predetermined range.
Claims5
A method comprising:
- adjusting a bias voltage applied to one or more non-volatile memory cells in an artificial neural network;
- performing a performance target check on the one or more non-volatile memory cells in the artificial neural network;
- and
- repeating the adjusting and performing until the performance target check indicates an electrical parameter is within a predetermined range, wherein the electrical parameter is a neuron current or an averaged neuron current.
1 more element
A system comprising:
- a bias control circuit;
- an array providing a sampled neuron current;
- and
- a current source coupled to the array;
- wherein a control gate bias or erase gate bias applied to the array is adjusted by the bias control circuit until the sampled neuron current is equal to current of the current source.
2 more elements
A system comprising:
- a bias control circuit;
- an array providing a sampled neuron current;
- and
- a resistor comprising a first terminal coupled to the array at a node and a second terminal coupled to ground;
- wherein a control gate bias or erase gate bias applied to the array is adjusted by the bias control circuit until a voltage at the node equals a reference voltage.
2 more elements
Dependencies are inferred from the claim text.