hxtorch.spiking

Modules

hxtorch.spiking.backend
hxtorch.spiking.datasets
hxtorch.spiking.execution_info
hxtorch.spiking.execution_instance	Definition of ExecutionInstance, wrapping grenade.common.ExecutionInstanceID, and providing functionality for chip instance configuration
hxtorch.spiking.experiment	Defining basic types to create hw-executable instances
hxtorch.spiking.functional
hxtorch.spiking.handle	Defining tensor handles able to hold references to tensors for lazy assignment after hardware data acquisition
hxtorch.spiking.modules
hxtorch.spiking.morphology	User defined neuron morphologies.
hxtorch.spiking.neuron_placement	Defining neuron placement allocator
hxtorch.spiking.observables	Hardware observables object
hxtorch.spiking.parameter	Generic parameter object holding hardware configurable neuron parameters.
hxtorch.spiking.run(experiment, runtime)	Execute the given experiment.
hxtorch.spiking.transforms
hxtorch.spiking.utils

Classes

AELIF(size, experiment, leak, reset, …)	Layer of neurons with configurable dynamics up to adaptive exponential leaky integrate-and-fire complexity.
BatchDropout(size, dropout, experiment)	Batch dropout layer
ConversionConfig(dt, calib_path, …)	Configuration for the conversion of NIRGraph to hxtorch SNN.
ExecutionInstance(calib_path, str]] = None, …)
Experiment(mock, dt[, hw_routing_func])	Experiment class for describing experiments on hardware
HXBaseExperimentModule(experiment)
HXModule(experiment, execution_instance, …)	PyTorch module supplying basic functionality for elements of SNNs that do have a representation on hardware
HXModuleWrapper(experiment, **modules)	Class to wrap HXModules
HXParameter(value, float, int])
HXTransformedModelParameter(model_value, …)
Handle(*args, **kwargs)	Factory for classes which are to be used as custom handles for observable data, depending on the specific observables a module deals with.
InputNeuron(size, experiment, …)	Spike source generating spikes at the times [ms] given in the spike_times array.
LI(size, experiment, leak, tau_mem, tau_syn, …)	Layer of leaky integrator neurons
LIF(size, experiment, leak, reset, …)	Layer of leaky integrate-and-fire neurons.
LIFObservables	alias of types.Handle_current_membrane_cadc_membrane_madc_spikes
LIObservables	alias of types.Handle_current_membrane_cadc_membrane_madc
MixedHXModelParameter(model_value, float, …)
ModelParameter(model_value, float, int])
NeuronExp(size, experiment, leak, reset, …)	Neuron layer with exponential Euler integration scheme.
ReadoutNeuronExp(size, experiment, leak, …)	Neuron layer with exponential Euler integration scheme.
SparseSynapse(connections, experiment, …)	Sparse synapse layer
Synapse(in_features, out_features, …)	Synapse layer
SynapseHandle	alias of types.Handle_graded_spikes
TensorHandle	alias of types.Handle_tensor

Functions

hxtorch.spiking.from_nir(graph: nir.ir.graph.NIRGraph, cfg: hxtorch.spiking.utils.from_nir.ConversionConfig = None) → torch.nn.modules.module.Module

Converts a NIRGraph to an hxtorch module.

Limitations

• Only NIRGraphs with exactly one Input and one Output node

• Only Linear, CubaLI, and CubaLIF nodes are supported

hxtorch.spiking.run(experiment: hxtorch.spiking.experiment.Experiment, runtime: Optional[int, None]) → Optional[hxtorch.spiking.execution_info.ExecutionInfo, None]

Execute the given experiment.

TODO: Why is this a standalone function?

Parameters

• experiment – The experiment representing the computational graph to be executed on hardware and/or in software.

• runtime – Only relevant for hardware experiments. Indicates the runtime resolved with experiment.dt.

hxtorch.spiking.to_nir(snn: hxtorch.spiking.utils.to_nir.SNN, input_sample) → nir.ir.graph.NIRGraph

Convert a hxtorch SNN to a NIR graph.

Parameters

• snn – The hxtorch SNN to convert, where snn.exp is the experiment object. Furthermore the SNN must use modules that are convertible to NIR (e.g. Synapse, AELIF).

• input_sample – A single input sample to the SNN.