def _write_synapse_parameters(self, spec, machine_vertex, machine_graph,
graph_mapper, post_slices, post_slice_index,
post_vertex_slice, input_type,
machine_time_step):
# Get the ring buffer shifts and scaling factors
weight_scale = input_type.get_global_weight_scale()
ring_buffer_shifts = self._get_ring_buffer_to_input_left_shifts(
machine_vertex, machine_graph, graph_mapper, post_slices,
post_slice_index, post_vertex_slice, machine_time_step,
weight_scale)
spec.switch_write_focus(
region=constants.POPULATION_BASED_REGIONS.SYNAPSE_PARAMS.value)
utility_calls.write_parameters_per_neuron(
spec, post_vertex_slice,
self._synapse_type.get_synapse_type_parameters())
spec.write_array(ring_buffer_shifts)
weight_scales = numpy.array([
self._get_weight_scale(r) * weight_scale
for r in ring_buffer_shifts
])
return weight_scales
def regenerate_data_specification(self, spec, placement, machine_time_step,
time_scale_factor, vertex_slice):
spec.reserve_memory_region(
region=POPULATION_BASED_REGIONS.SYNAPSE_PARAMS.value,
size=self._get_synapse_params_size(vertex_slice),
label='SynapseParams')
spec.switch_write_focus(POPULATION_BASED_REGIONS.SYNAPSE_PARAMS.value)
write_parameters_per_neuron(
spec, vertex_slice,
self._synapse_type.get_synapse_type_parameters())
def _write_synapse_parameters(
self, spec, subvertex, subgraph, graph_mapper, post_slices,
post_slice_index, post_vertex_slice, input_type):
# Get the ring buffer shifts and scaling factors
weight_scale = input_type.get_global_weight_scale()
ring_buffer_shifts = self._get_ring_buffer_to_input_left_shifts(
subvertex, subgraph, graph_mapper, post_slices, post_slice_index,
post_vertex_slice, self._machine_time_step, weight_scale)
spec.switch_write_focus(
region=constants.POPULATION_BASED_REGIONS.SYNAPSE_PARAMS.value)
utility_calls.write_parameters_per_neuron(
spec, post_vertex_slice,
self._synapse_type.get_synapse_type_parameters())
spec.write_array(ring_buffer_shifts)
weight_scales = numpy.array([
self._get_weight_scale(r) * weight_scale
for r in ring_buffer_shifts])
return weight_scales
def _write_neuron_parameters(self, spec, key, vertex_slice):
n_atoms = (vertex_slice.hi_atom - vertex_slice.lo_atom) + 1
spec.comment(
"\nWriting Neuron Parameters for {} Neurons:\n".format(n_atoms))
# Set the focus to the memory region 2 (neuron parameters):
spec.switch_write_focus(
region=constants.POPULATION_BASED_REGIONS.NEURON_PARAMS.value)
# Write whether the key is to be used, and then the key, or 0 if it
# isn't to be used
if key is None:
spec.write_value(data=0)
spec.write_value(data=0)
else:
spec.write_value(data=1)
spec.write_value(data=key)
# Write the number of neurons in the block:
spec.write_value(data=n_atoms)
# Write the size of the incoming spike buffer
spec.write_value(data=self._incoming_spike_buffer_size)
# Write the global parameters
global_params = self._neuron_model.get_global_parameters()
for param in global_params:
spec.write_value(data=param.get_value(),
data_type=param.get_dataspec_datatype())
# Write the neuron parameters
utility_calls.write_parameters_per_neuron(
spec, vertex_slice, self._neuron_model.get_neural_parameters())
# Write the input type parameters
utility_calls.write_parameters_per_neuron(
spec, vertex_slice, self._input_type.get_input_type_parameters())
# Write the additional input parameters
if self._additional_input is not None:
utility_calls.write_parameters_per_neuron(
spec, vertex_slice, self._additional_input.get_parameters())
# Write the threshold type parameters
utility_calls.write_parameters_per_neuron(
spec, vertex_slice,
self._threshold_type.get_threshold_parameters())
def _write_synapse_parameters(
self, spec, subvertex, subgraph, graph_mapper, vertex_slice):
# Get the ring buffer shifts and scaling factors
ring_buffer_shifts = self._get_ring_buffer_to_input_left_shifts(
subvertex, subgraph, graph_mapper, self._machine_time_step)
weight_scales = [self._get_weight_scale(r) for r in ring_buffer_shifts]
# update projections for future use
in_partitioned_edges = subgraph.incoming_subedges_from_subvertex(
subvertex)
for partitioned_edge in in_partitioned_edges:
partitioned_edge.weight_scales_setter(weight_scales)
spec.switch_write_focus(
region=constants.POPULATION_BASED_REGIONS.SYNAPSE_PARAMS.value)
utility_calls.write_parameters_per_neuron(
spec, vertex_slice,
self._synapse_type.get_synapse_type_parameters())
spec.write_array(ring_buffer_shifts)
return weight_scales
def _write_neuron_parameters(
self, spec, key, vertex_slice):
n_atoms = (vertex_slice.hi_atom - vertex_slice.lo_atom) + 1
spec.comment("\nWriting Neuron Parameters for {} Neurons:\n".format(
n_atoms))
# Set the focus to the memory region 2 (neuron parameters):
spec.switch_write_focus(
region=constants.POPULATION_BASED_REGIONS.NEURON_PARAMS.value)
# Write whether the key is to be used, and then the key, or 0 if it
# isn't to be used
if key is None:
spec.write_value(data=0)
spec.write_value(data=0)
else:
spec.write_value(data=1)
spec.write_value(data=key)
# Write the number of neurons in the block:
spec.write_value(data=n_atoms)
# Write the global parameters
global_params = self._neuron_model.get_global_parameters()
for param in global_params:
spec.write_value(data=param.get_value(),
data_type=param.get_dataspec_datatype())
# Write the neuron parameters
utility_calls.write_parameters_per_neuron(
spec, vertex_slice, self._neuron_model.get_neural_parameters())
# Write the input type parameters
utility_calls.write_parameters_per_neuron(
spec, vertex_slice, self._input_type.get_input_type_parameters())
# Write the additional input parameters
if self._additional_input is not None:
utility_calls.write_parameters_per_neuron(
spec, vertex_slice, self._additional_input.get_parameters())
# Write the threshold type parameters
utility_calls.write_parameters_per_neuron(
spec, vertex_slice,
self._threshold_type.get_threshold_parameters())
def _write_neuron_parameters(self, spec, key, vertex_slice,
machine_time_step, time_scale_factor):
# pylint: disable=too-many-arguments
n_atoms = (vertex_slice.hi_atom - vertex_slice.lo_atom) + 1
spec.comment(
"\nWriting Neuron Parameters for {} Neurons:\n".format(n_atoms))
# Set the focus to the memory region 2 (neuron parameters):
spec.switch_write_focus(
region=constants.POPULATION_BASED_REGIONS.NEURON_PARAMS.value)
# Write the random back off value
spec.write_value(
random.randint(0, AbstractPopulationVertex._n_vertices))
# Write the number of microseconds between sending spikes
time_between_spikes = ((machine_time_step * time_scale_factor) /
(n_atoms * 2.0))
spec.write_value(data=int(time_between_spikes))
# Write whether the key is to be used, and then the key, or 0 if it
# isn't to be used
if key is None:
spec.write_value(data=0)
spec.write_value(data=0)
else:
spec.write_value(data=1)
spec.write_value(data=key)
# Write the number of neurons in the block:
spec.write_value(data=n_atoms)
# Write the size of the incoming spike buffer
spec.write_value(data=self._incoming_spike_buffer_size)
# Write the recording rates and sizes
record_globals = self._neuron_recorder.get_global_parameters(
vertex_slice)
for param in record_globals:
spec.write_value(data=param.get_value(),
data_type=param.get_dataspec_datatype())
# Write the index parameters
indexes = self._neuron_recorder.get_index_parameters(vertex_slice)
utility_calls.write_parameters_per_neuron(spec,
vertex_slice,
indexes,
slice_paramaters=True)
# Write the global parameters
global_params = self._neuron_model.get_global_parameters()
for param in global_params:
spec.write_value(data=param.get_value(),
data_type=param.get_dataspec_datatype())
# Write the neuron parameters
utility_calls.write_parameters_per_neuron(
spec, vertex_slice, self._neuron_model.get_neural_parameters())
# Write the input type parameters
utility_calls.write_parameters_per_neuron(
spec, vertex_slice, self._input_type.get_input_type_parameters())
# Write the additional input parameters
if self._additional_input is not None:
utility_calls.write_parameters_per_neuron(
spec, vertex_slice, self._additional_input.get_parameters())
# Write the threshold type parameters
utility_calls.write_parameters_per_neuron(
spec, vertex_slice,
self._threshold_type.get_threshold_parameters())
