hxtorch.spiking

Modules

hxtorch.spiking.backend
hxtorch.spiking.datasets
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.run(experiment, runtime)	Execute the given experiment.
hxtorch.spiking.transforms
hxtorch.spiking.utils

Classes

BatchDropout(size, dropout, experiment, …)	Batch dropout layer
ExecutionInstance(calib_path, str]] = None, …)
Experiment(mock, dt[, hw_routing_func])	Experiment class for describing experiments on hardware
HXBaseExperimentModule(experiment)
HXModule(experiment, func, …)	PyTorch module supplying basic functionality for elements of SNNs that do have a representation on hardware
HXModuleWrapper(experiment, modules, func)	Class to wrap HXModules
IAFNeuron(size, experiment, func, …[, …])	Integrate-and-fire neuron Caveat: For execution on hardware, this module can only be used in conjunction with a preceding Synapse module.
InputNeuron(size, experiment, execution_instance)	Spike source generating spikes at the times [ms] given in the spike_times array.
Neuron(size, experiment, func, …[, …])	Neuron layer
NeuronHandle(spikes, v_cadc, current, v_madc)	Specialization for HX neuron observables
ReadoutNeuron(size, experiment, func, …)	Readout neuron layer
ReadoutNeuronHandle(v_cadc, current, v_madc)	Specialization for HX neuron observables
SparseSynapse(connections, experiment, func, …)	Sparse synapse layer
Synapse(in_features, out_features, …)	Synapse layer
SynapseHandle(graded_spikes)	Specialization for HX synapses
TensorHandle()	Base class for HX tensor handles.

Functions

hxtorch.spiking.run(experiment: hxtorch.spiking.experiment.Experiment, runtime: Optional[int]) → Optional[_pygrenade_vx_signal_flow.ExecutionTimeInfo]

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.