lava.proc.sdn

lava.proc.sdn.models

digraph inheritancedf7a130e54 { bgcolor=transparent; rankdir=TB; size=""; "ABC" [fillcolor=white,fontname="Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans",fontsize=10,height=0.25,shape=box,style="setlinewidth(0.5),filled",tooltip="Helper class that provides a standard way to create an ABC using"]; "AbstractDeltaModel" [URL="../lava/lava.proc.sdn.html#lava.proc.sdn.models.AbstractDeltaModel",fillcolor=white,fontname="Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans",fontsize=10,height=0.25,shape=box,style="setlinewidth(0.5),filled",target="_top"]; "PyLoihiProcessModel" -> "AbstractDeltaModel" [arrowsize=0.5,style="setlinewidth(0.5)"]; "AbstractProcessModel" [URL="../lava.magma.core.model.html#lava.magma.core.model.model.AbstractProcessModel",fillcolor=white,fontname="Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans",fontsize=10,height=0.25,shape=box,style="setlinewidth(0.5),filled",target="_top",tooltip="Represents a model that implements the behavior of a Process."]; "ABC" -> "AbstractProcessModel" [arrowsize=0.5,style="setlinewidth(0.5)"]; "AbstractPyProcessModel" [URL="../lava.magma.core.model.py.html#lava.magma.core.model.py.model.AbstractPyProcessModel",fillcolor=white,fontname="Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans",fontsize=10,height=0.25,shape=box,style="setlinewidth(0.5),filled",target="_top",tooltip="Abstract interface for Python ProcessModels."]; "AbstractProcessModel" -> "AbstractPyProcessModel" [arrowsize=0.5,style="setlinewidth(0.5)"]; "ABC" -> "AbstractPyProcessModel" [arrowsize=0.5,style="setlinewidth(0.5)"]; "AbstractSigmaDeltaModel" [URL="../lava/lava.proc.sdn.html#lava.proc.sdn.models.AbstractSigmaDeltaModel",fillcolor=white,fontname="Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans",fontsize=10,height=0.25,shape=box,style="setlinewidth(0.5),filled",target="_top"]; "AbstractSigmaModel" -> "AbstractSigmaDeltaModel" [arrowsize=0.5,style="setlinewidth(0.5)"]; "AbstractDeltaModel" -> "AbstractSigmaDeltaModel" [arrowsize=0.5,style="setlinewidth(0.5)"]; "AbstractSigmaModel" [URL="../lava/lava.proc.sdn.html#lava.proc.sdn.models.AbstractSigmaModel",fillcolor=white,fontname="Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans",fontsize=10,height=0.25,shape=box,style="setlinewidth(0.5),filled",target="_top"]; "PyLoihiProcessModel" -> "AbstractSigmaModel" [arrowsize=0.5,style="setlinewidth(0.5)"]; "PyDeltaModelFixed" [URL="../lava/lava.proc.sdn.html#lava.proc.sdn.models.PyDeltaModelFixed",fillcolor=white,fontname="Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans",fontsize=10,height=0.25,shape=box,style="setlinewidth(0.5),filled",target="_top",tooltip="Fixed point implementation of Delta encoding."]; "AbstractDeltaModel" -> "PyDeltaModelFixed" [arrowsize=0.5,style="setlinewidth(0.5)"]; "PyDeltaModelFloat" [URL="../lava/lava.proc.sdn.html#lava.proc.sdn.models.PyDeltaModelFloat",fillcolor=white,fontname="Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans",fontsize=10,height=0.25,shape=box,style="setlinewidth(0.5),filled",target="_top",tooltip="Floating point implementation of Delta encoding."]; "AbstractDeltaModel" -> "PyDeltaModelFloat" [arrowsize=0.5,style="setlinewidth(0.5)"]; "PyLoihiProcessModel" [URL="../lava.magma.core.model.py.html#lava.magma.core.model.py.model.PyLoihiProcessModel",fillcolor=white,fontname="Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans",fontsize=10,height=0.25,shape=box,style="setlinewidth(0.5),filled",target="_top",tooltip="ProcessModel to simulate a Process on Loihi using CPU."]; "AbstractPyProcessModel" -> "PyLoihiProcessModel" [arrowsize=0.5,style="setlinewidth(0.5)"]; "PySigmaDeltaModelFixed" [URL="../lava/lava.proc.sdn.html#lava.proc.sdn.models.PySigmaDeltaModelFixed",fillcolor=white,fontname="Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans",fontsize=10,height=0.25,shape=box,style="setlinewidth(0.5),filled",target="_top",tooltip="Fixed point implementation of Sigma Delta neuron."]; "AbstractSigmaDeltaModel" -> "PySigmaDeltaModelFixed" [arrowsize=0.5,style="setlinewidth(0.5)"]; "PySigmaDeltaModelFloat" [URL="../lava/lava.proc.sdn.html#lava.proc.sdn.models.PySigmaDeltaModelFloat",fillcolor=white,fontname="Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans",fontsize=10,height=0.25,shape=box,style="setlinewidth(0.5),filled",target="_top",tooltip="Floating point implementation of Sigma Delta neuron."]; "AbstractSigmaDeltaModel" -> "PySigmaDeltaModelFloat" [arrowsize=0.5,style="setlinewidth(0.5)"]; "PySigmaModelFixed" [URL="../lava/lava.proc.sdn.html#lava.proc.sdn.models.PySigmaModelFixed",fillcolor=white,fontname="Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans",fontsize=10,height=0.25,shape=box,style="setlinewidth(0.5),filled",target="_top",tooltip="Fixed point implementation of Sigma decoding"]; "AbstractSigmaModel" -> "PySigmaModelFixed" [arrowsize=0.5,style="setlinewidth(0.5)"]; "PySigmaModelFloat" [URL="../lava/lava.proc.sdn.html#lava.proc.sdn.models.PySigmaModelFloat",fillcolor=white,fontname="Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans",fontsize=10,height=0.25,shape=box,style="setlinewidth(0.5),filled",target="_top",tooltip="Floating point implementation of Sigma decoding"]; "AbstractSigmaModel" -> "PySigmaModelFloat" [arrowsize=0.5,style="setlinewidth(0.5)"]; }

class lava.proc.sdn.models.AbstractDeltaModel(proc_params=None)

Bases: PyLoihiProcessModel

a_in = None

act = None

cum_error = None

delta_dynamics(act_data)

Delta encodind dynamics method

Parameters:

act_data (np.ndarray) – data to be encoded

Returns:

delta encoded data

Return type:

np.ndarray

error = None

residue = None

s_out = None

spike_exp = None

state_exp = None

vth = None

class lava.proc.sdn.models.AbstractSigmaDeltaModel(proc_params)

Bases: AbstractSigmaModel, AbstractDeltaModel

a_in = None

act = None

activation_dynamics(sigma_data)

Sigma Delta activation dynamics. UNIT and RELU activations are supported.

Parameters:

sigma_data (np.ndarray) – sigma decoded data

Returns:

activation output

Return type:

np.ndarray

Raises:

NotImplementedError – if activation mode other than UNIT or RELU is encountered.

bias = None

cum_error = None

dynamics(a_in_data)

Return type:

ndarray

error = None

residue = None

s_out = None

sigma = None

spike_exp = None

state_exp = None

vth = None

class lava.proc.sdn.models.AbstractSigmaModel(proc_params=None)

Bases: PyLoihiProcessModel

a_in = None

a_out = None

run_spk()

Function that runs in Spiking Phase

Return type:

None

sigma = None

sigma_dynamics(a_in_data)

Sigma decoding dynamics method

Parameters:

a_in_data (np.ndarray) – Input data

Returns:

decoded data

Return type:

np.ndarray

class lava.proc.sdn.models.PyDeltaModelFixed(proc_params=None)

Bases: AbstractDeltaModel

Fixed point implementation of Delta encoding.

a_in = LavaPyType(cls=<class 'lava.magma.core.model.py.ports.PyInPortVectorDense'>, d_type=<class 'numpy.int32'>, precision=24)

act: ndarray = LavaPyType(cls=<class 'numpy.ndarray'>, d_type=<class 'numpy.int32'>, precision=24)

cum_error: ndarray = LavaPyType(cls=<class 'numpy.ndarray'>, d_type=<class 'bool'>, precision=1)

error: ndarray = LavaPyType(cls=<class 'numpy.ndarray'>, d_type=<class 'numpy.int32'>, precision=24)

implements_process

alias of Delta

implements_protocol

alias of LoihiProtocol

required_resources: ty.List[ty.Type[AbstractResource]] = [<class 'lava.magma.core.resources.CPU'>]

residue: ndarray = LavaPyType(cls=<class 'numpy.ndarray'>, d_type=<class 'numpy.int32'>, precision=24)

run_spk()

Function that runs in Spiking Phase

Return type:

None

s_out = LavaPyType(cls=<class 'lava.magma.core.model.py.ports.PyOutPortVectorDense'>, d_type=<class 'numpy.int32'>, precision=24)

sigma: ndarray = LavaPyType(cls=<class 'numpy.ndarray'>, d_type=<class 'numpy.int32'>, precision=24)

spike_exp: ndarray = LavaPyType(cls=<class 'numpy.ndarray'>, d_type=<class 'numpy.int32'>, precision=3)

state_exp: ndarray = LavaPyType(cls=<class 'numpy.ndarray'>, d_type=<class 'numpy.int32'>, precision=3)

tags: ty.List[str] = ['fixed_pt']

vth: ndarray = LavaPyType(cls=<class 'numpy.ndarray'>, d_type=<class 'numpy.int32'>, precision=24)

class lava.proc.sdn.models.PyDeltaModelFloat(proc_params=None)

Bases: AbstractDeltaModel

Floating point implementation of Delta encoding.

a_in = LavaPyType(cls=<class 'lava.magma.core.model.py.ports.PyInPortVectorDense'>, d_type=<class 'float'>, precision=None)

act: ndarray = LavaPyType(cls=<class 'numpy.ndarray'>, d_type=<class 'float'>, precision=None)

cum_error: ndarray = LavaPyType(cls=<class 'numpy.ndarray'>, d_type=<class 'bool'>, precision=1)

error: ndarray = LavaPyType(cls=<class 'numpy.ndarray'>, d_type=<class 'float'>, precision=None)

implements_process

alias of Delta

implements_protocol

alias of LoihiProtocol

required_resources: ty.List[ty.Type[AbstractResource]] = [<class 'lava.magma.core.resources.CPU'>]

residue: ndarray = LavaPyType(cls=<class 'numpy.ndarray'>, d_type=<class 'float'>, precision=None)

run_spk()

Function that runs in Spiking Phase

Return type:

None

s_out = LavaPyType(cls=<class 'lava.magma.core.model.py.ports.PyOutPortVectorDense'>, d_type=<class 'float'>, precision=None)

sigma: ndarray = LavaPyType(cls=<class 'numpy.ndarray'>, d_type=<class 'float'>, precision=None)

spike_exp: ndarray = LavaPyType(cls=<class 'numpy.ndarray'>, d_type=<class 'numpy.int32'>, precision=3)

state_exp: ndarray = LavaPyType(cls=<class 'numpy.ndarray'>, d_type=<class 'numpy.int32'>, precision=3)

tags: ty.List[str] = ['floating_pt']

vth: ndarray = LavaPyType(cls=<class 'numpy.ndarray'>, d_type=<class 'float'>, precision=None)

class lava.proc.sdn.models.PySigmaDeltaModelFixed(proc_params)

Bases: AbstractSigmaDeltaModel

Fixed point implementation of Sigma Delta neuron.

a_in = LavaPyType(cls=<class 'lava.magma.core.model.py.ports.PyInPortVectorDense'>, d_type=<class 'numpy.int32'>, precision=24)

act: ndarray = LavaPyType(cls=<class 'numpy.ndarray'>, d_type=<class 'numpy.int32'>, precision=24)

bias: ndarray = LavaPyType(cls=<class 'numpy.ndarray'>, d_type=<class 'numpy.int32'>, precision=16)

cum_error: ndarray = LavaPyType(cls=<class 'numpy.ndarray'>, d_type=<class 'bool'>, precision=1)

error: ndarray = LavaPyType(cls=<class 'numpy.ndarray'>, d_type=<class 'numpy.int32'>, precision=24)

implements_process

alias of SigmaDelta

implements_protocol

alias of LoihiProtocol

required_resources: ty.List[ty.Type[AbstractResource]] = [<class 'lava.magma.core.resources.CPU'>]

residue: ndarray = LavaPyType(cls=<class 'numpy.ndarray'>, d_type=<class 'numpy.int32'>, precision=24)

run_spk()

Function that runs in Spiking Phase

Return type:

None

s_out = LavaPyType(cls=<class 'lava.magma.core.model.py.ports.PyOutPortVectorDense'>, d_type=<class 'numpy.int32'>, precision=24)

sigma: ndarray = LavaPyType(cls=<class 'numpy.ndarray'>, d_type=<class 'numpy.int32'>, precision=24)

spike_exp: ndarray = LavaPyType(cls=<class 'numpy.ndarray'>, d_type=<class 'numpy.int32'>, precision=3)

state_exp: ndarray = LavaPyType(cls=<class 'numpy.ndarray'>, d_type=<class 'numpy.int32'>, precision=3)

tags: ty.List[str] = ['fixed_pt']

vth: ndarray = LavaPyType(cls=<class 'numpy.ndarray'>, d_type=<class 'numpy.int32'>, precision=24)

class lava.proc.sdn.models.PySigmaDeltaModelFloat(proc_params)

Bases: AbstractSigmaDeltaModel

Floating point implementation of Sigma Delta neuron.

a_in = LavaPyType(cls=<class 'lava.magma.core.model.py.ports.PyInPortVectorDense'>, d_type=<class 'float'>, precision=None)

act: ndarray = LavaPyType(cls=<class 'numpy.ndarray'>, d_type=<class 'float'>, precision=None)

bias: ndarray = LavaPyType(cls=<class 'numpy.ndarray'>, d_type=<class 'float'>, precision=None)

cum_error: ndarray = LavaPyType(cls=<class 'numpy.ndarray'>, d_type=<class 'bool'>, precision=1)

error: ndarray = LavaPyType(cls=<class 'numpy.ndarray'>, d_type=<class 'float'>, precision=None)

implements_process

alias of SigmaDelta

implements_protocol

alias of LoihiProtocol

required_resources: ty.List[ty.Type[AbstractResource]] = [<class 'lava.magma.core.resources.CPU'>]

residue: ndarray = LavaPyType(cls=<class 'numpy.ndarray'>, d_type=<class 'float'>, precision=None)

run_spk()

Function that runs in Spiking Phase

Return type:

None

s_out = LavaPyType(cls=<class 'lava.magma.core.model.py.ports.PyOutPortVectorDense'>, d_type=<class 'float'>, precision=None)

sigma: ndarray = LavaPyType(cls=<class 'numpy.ndarray'>, d_type=<class 'float'>, precision=None)

spike_exp: ndarray = LavaPyType(cls=<class 'numpy.ndarray'>, d_type=<class 'numpy.int32'>, precision=3)

state_exp: ndarray = LavaPyType(cls=<class 'numpy.ndarray'>, d_type=<class 'numpy.int32'>, precision=3)

tags: ty.List[str] = ['floating_pt']

vth: ndarray = LavaPyType(cls=<class 'numpy.ndarray'>, d_type=<class 'float'>, precision=None)

class lava.proc.sdn.models.PySigmaModelFixed(proc_params=None)

Bases: AbstractSigmaModel

Fixed point implementation of Sigma decoding

a_in = LavaPyType(cls=<class 'lava.magma.core.model.py.ports.PyInPortVectorDense'>, d_type=<class 'numpy.int32'>, precision=24)

implements_process

alias of Sigma

implements_protocol

alias of LoihiProtocol

required_resources: ty.List[ty.Type[AbstractResource]] = [<class 'lava.magma.core.resources.CPU'>]

s_out = LavaPyType(cls=<class 'lava.magma.core.model.py.ports.PyOutPortVectorDense'>, d_type=<class 'numpy.int32'>, precision=24)

sigma: ndarray = LavaPyType(cls=<class 'numpy.ndarray'>, d_type=<class 'numpy.int32'>, precision=24)

tags: ty.List[str] = ['fixed_pt']

class lava.proc.sdn.models.PySigmaModelFloat(proc_params=None)

Bases: AbstractSigmaModel

Floating point implementation of Sigma decoding

a_in = LavaPyType(cls=<class 'lava.magma.core.model.py.ports.PyInPortVectorDense'>, d_type=<class 'float'>, precision=None)

implements_process

alias of Sigma

implements_protocol

alias of LoihiProtocol

required_resources: ty.List[ty.Type[AbstractResource]] = [<class 'lava.magma.core.resources.CPU'>]

s_out = LavaPyType(cls=<class 'lava.magma.core.model.py.ports.PyOutPortVectorDense'>, d_type=<class 'float'>, precision=None)

sigma: ndarray = LavaPyType(cls=<class 'numpy.ndarray'>, d_type=<class 'float'>, precision=None)

tags: ty.List[str] = ['floating_pt']

lava.proc.sdn.models.ReLU(x)

ReLU activation implementation

Parameters:

x (np.ndarray) – input array

Returns:

output array

Return type:

np.ndarray

lava.proc.sdn.process

digraph inheritance78568b1eac { bgcolor=transparent; rankdir=TB; size=""; "AbstractProcess" [URL="../lava.magma.core.process.html#lava.magma.core.process.process.AbstractProcess",fillcolor=white,fontname="Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans",fontsize=10,height=0.25,shape=box,style="setlinewidth(0.5),filled",target="_top",tooltip="The notion of a Process is inspired by the Communicating Sequential"]; "ActivationMode" [URL="../lava/lava.proc.sdn.html#lava.proc.sdn.process.ActivationMode",fillcolor=white,fontname="Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans",fontsize=10,height=0.25,shape=box,style="setlinewidth(0.5),filled",target="_top",tooltip="Enum for synapse sigma delta activation mode. Options are"]; "IntEnum" -> "ActivationMode" [arrowsize=0.5,style="setlinewidth(0.5)"]; "Delta" [URL="../lava/lava.proc.sdn.html#lava.proc.sdn.process.Delta",fillcolor=white,fontname="Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans",fontsize=10,height=0.25,shape=box,style="setlinewidth(0.5),filled",target="_top"]; "AbstractProcess" -> "Delta" [arrowsize=0.5,style="setlinewidth(0.5)"]; "Enum" [fillcolor=white,fontname="Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans",fontsize=10,height=0.25,shape=box,style="setlinewidth(0.5),filled",tooltip="Generic enumeration."]; "IntEnum" [fillcolor=white,fontname="Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans",fontsize=10,height=0.25,shape=box,style="setlinewidth(0.5),filled",tooltip="Enum where members are also (and must be) ints"]; "Enum" -> "IntEnum" [arrowsize=0.5,style="setlinewidth(0.5)"]; "Sigma" [URL="../lava/lava.proc.sdn.html#lava.proc.sdn.process.Sigma",fillcolor=white,fontname="Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans",fontsize=10,height=0.25,shape=box,style="setlinewidth(0.5),filled",target="_top"]; "AbstractProcess" -> "Sigma" [arrowsize=0.5,style="setlinewidth(0.5)"]; "SigmaDelta" [URL="../lava/lava.proc.sdn.html#lava.proc.sdn.process.SigmaDelta",fillcolor=white,fontname="Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans",fontsize=10,height=0.25,shape=box,style="setlinewidth(0.5),filled",target="_top"]; "AbstractProcess" -> "SigmaDelta" [arrowsize=0.5,style="setlinewidth(0.5)"]; }

class lava.proc.sdn.process.ActivationMode(value)

Bases: IntEnum

Enum for synapse sigma delta activation mode. Options are UNIT: 0 RELU: 1

RELU = 1

UNIT = 0

class lava.proc.sdn.process.Delta(*, shape, vth, cum_error=False, spike_exp=0, state_exp=0)

Bases: AbstractProcess

property shape: Tuple[int, ...]

Return shape of the Process.

class lava.proc.sdn.process.Sigma(*, shape)

Bases: AbstractProcess

property shape: Tuple[int, ...]

Return shape of the Process.

class lava.proc.sdn.process.SigmaDelta(*, shape, vth, bias=0, act_mode=ActivationMode.RELU, cum_error=False, spike_exp=0, state_exp=0)

Bases: AbstractProcess

property shape: Tuple[int, ...]

Return shape of the Process.