Plastic synapse management
AssigneeQUALCOMM Incorporated
InventorsVikram Gupta, Sarah Paige Gibson, Jeffrey Alexander Levin, Ravindra Manohar Patwardhan, Avijit Chakraborty, William Howard Constable, William Richard Bell, II
Life of this patent
Estimating the term…
Abstract
A method for managing synapse plasticity in a neural network includes converting a first set of synapses from a plastic synapse type to a fixed synapse type. The method may also include converting a second set of synapses from the fixed synapse type to the plastic synapse type.
Claims20
A method for managing synapse plasticity in an artificial neural network, comprising
- determining whether a number of plastic synapse types defined for the artificial neural network is greater than a hardware limit;
- and
- converting at least one of:
- a first set of synapses from a plastic synapse type to a fixed synapse type when the first set of synapses is designated as unused, a second set of synapses from the fixed synapse type to the plastic synapse type when the second set of synapses is designated as used, or a combination thereof, when the number of defined plastic synapse types is greater than the hardware limit, such that a number of active plastic synapse types is less than or equal to the hardware limit.
An apparatus for managing synapse plasticity in an artificial neural network, comprising:
- a memory unit;
- and
- at least one processor coupled to the memory unit, the at least one processor being configured:
- to determine whether a number of plastic synapse types defined for the artificial neural network is greater than a hardware limit;
- and
- to convert at least one of:
- a first set of synapses from a plastic synapse type to a fixed synapse type when the first set of synapses is designated as unused, a second set of synapses from the fixed synapse type to the plastic synapse type when the second set of synapses is designated as used, or a combination thereof, when the number of defined plastic synapse types is greater than the hardware limit, such that a number of active plastic synapse types is less than or equal to the hardware limit.
An apparatus for managing synapse plasticity in an artificial neural network, comprising
- means for determining whether a number of plastic synapse types defined for the artificial neural network is greater than a hardware limit of the artificial neural network;
- and
- means for converting at least one of:
- a first set of synapses from a plastic synapse type to a fixed synapse type when the first set of synapses is designated as unused, a second set of synapses from the fixed synapse type to the plastic synapse type when the second set of synapses is designated as used, or a combination thereof, when the number of defined plastic synapse types is greater than the hardware limit, such that a number of active plastic synapse types is less than or equal to the hardware limit.
A non-transitory computer-readable medium having program code recorded thereon for managing synapse plasticity in an artificial neural network, the program code comprising:
- program code to determine whether a number of plastic synapse types defined for the artificial neural network is greater than a hardware limit;
- and
- program code to convert at least one of:
- a first set of synapses from a plastic synapse type to a fixed synapse type when the first set of synapses is designated as unused, a second set of synapses from the fixed synapse type to the plastic synapse type when the second set of synapses is designated as used, or a combination thereof, when the number of defined plastic synapse types is greater than the hardware limit, such that a number of active plastic synapse types is less than or equal to the hardware limit.
Dependencies are inferred from the claim text.