Battery-based neural network weights
AssigneeInternational Business Machines Corporation
InventorsJoel P. de Souza, Yun Seog Lee, Ning Li, Devendra K. Sadana
Life of this patent
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Abstract
A controllable resistive element and methods for controlling the resistance of the same include a resistor layer formed in contact with a shared read/write electrode and a read electrode, the resistor layer having a resistivity that depends on a concentration of charge carrier ions. An electrolyte layer is formed on the resistor layer. A reservoir layer is formed on the electrolyte layer and in contact with a write electrode.
Claims16
A controllable resistive element, comprising:
- a resistor layer formed in direct contact with a shared read/write electrode, that is used for write operation and read operation, and in direct contact with a read electrode, which forms a first conductive path with the shared read/write electrode, the resistor layer having a resistivity that depends on a concentration of charge carrier ions;
- an electrolyte layer formed on the resistor layer;
- and
- a reservoir layer formed on the electrolyte layer and in contact with a write electrode, which forms a second conductive path with the shared read-write electrode.
A neural network processing system, comprising:
- a battery-based neural network, comprising:
- a set of one or more input neurons;
- one or more sets of hidden neurons, each set of hidden neurons comprising one or more hidden neurons;
- a set of one or more output neurons;
- and
- a plurality of weight arrays, each weight array being disposed between sets of neurons and each weight comprising:
- a resistor layer formed in direct contact with a shared read/write electrode, that is used for write operation and read operation, and in direct contact with a read electrode, which forms a first conductive path with the shared read/write electrode, the resistor layer having a resistivity that depends on a concentration of a charge carrier;
- an electrolyte layer formed on the resistor layer;
- and
- a reservoir layer formed on the electrolyte layer and in contact with a write electrode, which forms a second conductive path with the shared read-write electrode;
- and
- a weight control module configured to provide a voltage applied to the respective weights of the weight arrays.
A neural network processing system, comprising:
- a battery-based neural network, comprising:
- a set of one or more input neurons;
- one or more sets of hidden neurons, each set of hidden neurons comprising one or more hidden neurons;
- a set of one or more output neurons;
- and
- a plurality of weight arrays, each weight array being disposed between sets of neurons and each weight comprising:
- a resistor layer formed in direct contact with a shared read/write electrode, that is used for write operation and read operation, and in direct contact with a read electrode, which forms a first conductive path with the shared read/write electrode, the resistor layer having a resistivity that depends on a concentration of a charge carrier;
- an electrolyte layer formed on the resistor layer;
- and
- a reservoir layer formed on the electrolyte layer and in contact with a write electrode, which forms a second conductive path with the shared read-write electrode;
- and
- a weight control module configured to provide a voltage applied to the respective weights of the weight arrays and to wait a predetermined amount of time before applying a read voltage to the respective weights to allow charge carrier ions in the resistor layer to settle.
Dependencies are inferred from the claim text.