def make_vertices(self, model, *args, **kwargs):
"""Create vertices that will simulate the SDPTransmitter."""
# Build the system region
self._sys_region = SystemRegion(model.machine_timestep,
self.size_in, 1)
# Build the filter regions
in_sigs = model.get_signals_connections_to_object(self)
self._filter_region, self._routing_region = make_filter_regions(
in_sigs[InputPort.standard], model.dt, True,
model.keyspaces.filter_routing_tag
)
# Get the resources
resources = {
Cores: 1,
SDRAM: region_utils.sizeof_regions(
[self._sys_region, self._filter_region, self._routing_region],
None
)
}
# Create the vertex
self._vertex = Vertex(get_application("tx"), resources)
# Return the netlist specification
return netlistspec(self._vertex,
load_function=self.load_to_machine)
def make_vertices(self, model, *args, **kwargs):
"""Create vertices that will simulate the SDPTransmitter."""
# Build the system region
self._sys_region = SystemRegion(model.machine_timestep,
self.size_in, 1)
# Build the filter regions
in_sigs = model.get_signals_to_object(self)[InputPort.standard]
self._filter_region, self._routing_region = make_filter_regions(
in_sigs, model.dt, True, model.keyspaces.filter_routing_tag)
# Get the resources
resources = {
Cores: 1,
SDRAM: region_utils.sizeof_regions(
[self._sys_region, self._filter_region, self._routing_region],
None
)
}
# Create the vertex
self._vertex = Vertex(get_application("tx"), resources)
# Return the netlist specification
return netlistspec((self._vertex, ), # Tuple is required
load_function=self.load_to_machine)
def make_vertices(self, model, n_steps): # TODO remove n_steps
"""Construct the data which can be loaded into the memory of a
SpiNNaker machine.
"""
# Extract all the filters from the incoming connections to build the
# filter regions.
signals_conns = model.get_signals_to_object(self)[InputPort.standard]
self.filter_region, self.filter_routing_region = make_filter_regions(
signals_conns, model.dt, True, model.keyspaces.filter_routing_tag)
# Use a matrix region to record into (slightly unpleasant)
self.recording_region = regions.MatrixRegion(
np.zeros((self.size_in, n_steps), dtype=np.uint32)
)
# This isn't partitioned, so we just compute the SDRAM requirement and
# return a new vertex.
self.system_region = SystemRegion(model.machine_timestep, self.size_in)
self.regions = [None] * 15
self.regions[0] = self.system_region
self.regions[1] = self.filter_region
self.regions[2] = self.filter_routing_region
self.regions[14] = self.recording_region # **YUCK**
resources = {
Cores: 1,
SDRAM: regions.utils.sizeof_regions(self.regions, None)
}
self.vertex = Vertex(get_application("value_sink"), resources)
# Return the spec
return netlistspec(self.vertex, self.load_to_machine,
after_simulation_function=self.after_simulation)
def __init__(self, timestep, n_steps, input_slice,
filter_region, filter_routing_region):
"""Create a new vertex for a portion of a value sink."""
self.input_slice = input_slice
# Store the pre-existing regions and create new regions
self.regions = {
Regions.system: SystemRegion(timestep, input_slice),
Regions.filters: filter_region,
Regions.filter_routing: filter_routing_region,
Regions.recording: regions.WordRecordingRegion(n_steps),
}
# Store region arguments
w = input_slice.stop - input_slice.start
self.region_arguments = {
Regions.system: Args(),
Regions.filters: Args(filter_width=w),
Regions.filter_routing: Args(),
Regions.recording: Args(input_slice),
}
# Determine resources usage
resources = {
Cores: 1,
SDRAM: sizeof_regions_named(self.regions, self.region_arguments)
}
super(ValueSinkVertex, self).__init__(get_application("value_sink"),
resources)
def make_vertices(self, model, n_steps):
"""Create the vertices to be simulated on the machine."""
# Create the system region
self.system_region = SystemRegion(model.machine_timestep,
self.period is not None, n_steps)
# Get all the outgoing signals to determine how big the size out is and
# to build a list of keys.
sigs_conns = model.get_signals_connections_from_object(self)
if len(sigs_conns) == 0:
return netlistspec([])
keys = list()
self.conns_transforms = list()
for sig, conns in iteritems(sigs_conns[OutputPort.standard]):
assert len(conns) == 1, "Expected a 1:1 mapping"
# Add the keys for this connection
conn = conns[0]
transform, sig_keys = get_transform_keys(model, sig, conn)
keys.extend(sig_keys)
self.conns_transforms.append((conn, transform))
size_out = len(keys)
# Build the keys region
self.keys_region = regions.KeyspacesRegion(
keys, [regions.KeyField({"cluster": "cluster"})],
partitioned_by_atom=True
)
# Create the output region
self.output_region = regions.MatrixRegion(
np.zeros((n_steps, size_out)),
sliced_dimension=regions.MatrixPartitioning.columns
)
self.regions = [self.system_region, self.keys_region,
self.output_region]
# Partition by output dimension to create vertices
transmit_constraint = partition.Constraint(10)
sdram_constraint = partition.Constraint(8*2**20) # Max 8MiB
constraints = {
transmit_constraint: lambda s: s.stop - s.start,
sdram_constraint:
lambda s: regions.utils.sizeof_regions(self.regions, s),
}
for sl in partition.partition(slice(0, size_out), constraints):
# Determine the resources
resources = {
Cores: 1,
SDRAM: regions.utils.sizeof_regions(self.regions, sl),
}
vsl = VertexSlice(sl, get_application("value_source"), resources)
self.vertices.append(vsl)
# Return the vertices and callback methods
return netlistspec(self.vertices, self.load_to_machine,
self.before_simulation)
def __init__(self, vertex_index, cluster_slices, input_slice, output_slice,
ens_regions):
"""Create a new slice of an Ensemble.
Parameters
----------
vertex_index : int
Index of this vertex within the cluster.
cluster_slices : [slice, ...]
List of slices
input_slice : slice
Slice of the input space to be managed by this instance.
output_slice : slice
Slice of the output space to be managed by this instance.
"""
# Store the parameters
self.input_slice = input_slice
self.output_slice = output_slice
self.regions = ens_regions
# Get the specific neural slice we care about and information regarding
# the rest of the vertices in this cluster.
self.vertex_index = vertex_index
self.neuron_slice = cluster_slices[vertex_index]
self.n_vertices_in_cluster = len(cluster_slices)
self.n_neurons_in_cluster = (cluster_slices[-1].stop -
cluster_slices[0].start)
# Get the basic arguments for the regions that we'll be storing
self.region_arguments = _get_basic_region_arguments(
self.neuron_slice, self.output_slice, cluster_slices)
# Add some other arguments for the ensemble region
self.region_arguments[EnsembleRegions.ensemble].kwargs.update({
"population_id":
vertex_index,
"input_slice":
input_slice,
"neuron_slice":
self.neuron_slice,
"output_slice":
output_slice,
})
# Compute the SDRAM usage
sdram_usage = regions.utils.sizeof_regions_named(
self.regions, self.region_arguments)
# Prepare the vertex
application = "ensemble"
if ens_regions[EnsembleRegions.profiler].n_samples > 0:
# If profiling then use the profiled version of the application
application += "_profiled"
super(EnsembleSlice, self).__init__(get_application(application), {
Cores: 1,
SDRAM: sdram_usage
})
def make_vertices(self, model, n_steps):
"""Create the vertices to be simulated on the machine."""
# Create the system region
self.system_region = SystemRegion(model.machine_timestep,
self.period is not None, n_steps)
# Get all the outgoing signals to determine how big the size out is and
# to build a list of keys.
sigs_conns = model.get_signals_from_object(self)
if len(sigs_conns) == 0:
return netlistspec([])
keys = list()
self.transmission_parameters = list()
for sig, transmission_params in sigs_conns[OutputPort.standard]:
# Add the keys for this connection
transform, sig_keys = get_transform_keys(sig, transmission_params)
keys.extend(sig_keys)
self.transmission_parameters.append((transmission_params,
transform))
size_out = len(keys)
# Build the keys region
self.keys_region = regions.KeyspacesRegion(
keys, [regions.KeyField({"cluster": "cluster"})],
partitioned_by_atom=True
)
# Create the output region
self.output_region = regions.MatrixRegion(
np.zeros((n_steps, size_out)),
sliced_dimension=regions.MatrixPartitioning.columns
)
self.regions = [self.system_region, self.keys_region,
self.output_region]
# Partition by output dimension to create vertices
transmit_constraint = partition.Constraint(10)
sdram_constraint = partition.Constraint(8*2**20) # Max 8MiB
constraints = {
transmit_constraint: lambda s: s.stop - s.start,
sdram_constraint: (
lambda s: regions.utils.sizeof_regions(self.regions, s)),
}
for sl in partition.partition(slice(0, size_out), constraints):
# Determine the resources
resources = {
Cores: 1,
SDRAM: regions.utils.sizeof_regions(self.regions, sl),
}
vsl = VertexSlice(sl, self._label, get_application("value_source"),
resources)
self.vertices.append(vsl)
# Return the vertices and callback methods
return netlistspec(self.vertices, self.load_to_machine,
self.before_simulation)
def test_get_application(app_name):
with mock.patch.object(application, "pkg_resources") as pkg_resources:
pkg_resources.resource_filename.return_value = "Camelot"
# Get the application filename
assert application.get_application(app_name) == "Camelot"
pkg_resources.resource_filename.assert_called_once_with(
"nengo_spinnaker", "binaries/nengo_{}.aplx".format(app_name))
def test_get_application(app_name):
with mock.patch.object(application, "pkg_resources") as pkg_resources:
pkg_resources.resource_filename.return_value = "Camelot"
# Get the application filename
assert application.get_application(app_name) == "Camelot"
pkg_resources.resource_filename.assert_called_once_with(
"nengo_spinnaker", "binaries/nengo_{}.aplx".format(app_name)
)
def __init__(self, vertex_index, cluster_slices, input_slice, output_slice,
ens_regions):
"""Create a new slice of an Ensemble.
Parameters
----------
vertex_index : int
Index of this vertex within the cluster.
cluster_slices : [slice, ...]
List of slices
input_slice : slice
Slice of the input space to be managed by this instance.
output_slice : slice
Slice of the output space to be managed by this instance.
"""
# Store the parameters
self.input_slice = input_slice
self.output_slice = output_slice
self.regions = ens_regions
# Get the specific neural slice we care about and information regarding
# the rest of the vertices in this cluster.
self.vertex_index = vertex_index
self.neuron_slice = cluster_slices[vertex_index]
self.n_vertices_in_cluster = len(cluster_slices)
self.n_neurons_in_cluster = (cluster_slices[-1].stop -
cluster_slices[0].start)
# Get the basic arguments for the regions that we'll be storing
self.region_arguments = _get_basic_region_arguments(
self.neuron_slice, self.output_slice, cluster_slices
)
# Add some other arguments for the ensemble region
self.region_arguments[EnsembleRegions.ensemble].kwargs.update({
"population_id": vertex_index,
"input_slice": input_slice,
"neuron_slice": self.neuron_slice,
"output_slice": output_slice,
})
# Compute the SDRAM usage
sdram_usage = regions.utils.sizeof_regions_named(
self.regions, self.region_arguments)
# Prepare the vertex
application = "ensemble"
if ens_regions[EnsembleRegions.profiler].n_samples > 0:
# If profiling then use the profiled version of the application
application += "_profiled"
super(EnsembleSlice, self).__init__(get_application(application),
{Cores: 1, SDRAM: sdram_usage})
def make_vertices(self, model, *args, **kwargs):
"""Create vertices that will simulate the SDPReceiver."""
# NOTE This approach will result in more routes being created than are
# actually necessary; the way to avoid this is to modify how the
# builder deals with signals when creating netlists.
# Get all outgoing signals and their associated connections (this
# SHOULD be a 1:1 mapping)
out = model.get_signals_connections_from_object(self)
for signal, connections in six.iteritems(out[OutputPort.standard]):
assert len(connections) == 1, "Expecting a 1:1 mapping"
conn = connections[0]
# Get the transform, and from this the keys
transform = model.params[conn].transform
keys = [signal.keyspace(index=i) for i in
range(transform.shape[0])]
# Create a vertex for this connection (assuming its size out <= 64)
if len(keys) > 64:
raise NotImplementedError(
"Connection {!s} is too wide to transmit to SpiNNaker. "
"Consider breaking the connection up or making the "
"originating node a function of time Node.".format(conn)
)
# Create the regions for the system
sys_region = SystemRegion(model.machine_timestep, len(keys))
keys_region = KeyspacesRegion(keys,
[KeyField({"cluster": "cluster"})])
# Get the resources
resources = {
Cores: 1,
SDRAM: region_utils.sizeof_regions([sys_region, keys_region],
None)
}
# Create the vertex
v = self.connection_vertices[conn] = Vertex(get_application("rx"),
resources)
self._sys_regions[v] = sys_region
self._key_regions[v] = keys_region
# Return the netlist specification
return netlistspec(list(self.connection_vertices.values()),
load_function=self.load_to_machine)
def __init__(self, in_slice, out_slice, resources=dict(),
transmission_parameter_slices=list()):
super(ParallelFilterSlice, self).__init__(get_application("filter"),
resources)
# Store the slices
self.in_slice = in_slice
self.out_slice = out_slice
# Store which signal parameter slices we contain
self.transmission_params = list()
out_set = set(range(out_slice.start or 0,
out_slice.stop or 0,
out_slice.step or 1))
for transmission_params, outs in transmission_parameter_slices:
# If there is an intersection between the outs and the set of outs
# we're responsible for then store transmission parameters.
if out_set & outs:
self.transmission_params.append(transmission_params)
def make_vertices(self, model, *args, **kwargs):
"""Create vertices that will simulate the SDPReceiver."""
# NOTE This approach will result in more routes being created than are
# actually necessary; the way to avoid this is to modify how the
# builder deals with signals when creating netlists.
# Get all outgoing signals and their associated transmission parameters
for signal, transmission_params in \
model.get_signals_from_object(self)[OutputPort.standard]:
# Get the transform, and from this the keys
transform = transmission_params.full_transform(slice_out=False)
keys = [(signal, {"index": i}) for i in
range(transform.shape[0])]
# Create a vertex for this connection (assuming its size out <= 64)
if len(keys) > 64:
raise NotImplementedError(
"Connection is too wide to transmit to SpiNNaker. "
"Consider breaking the connection up or making the "
"originating node a function of time Node."
)
# Create the regions for the system
sys_region = SystemRegion(model.machine_timestep, len(keys))
keys_region = KeyspacesRegion(keys,
[KeyField({"cluster": "cluster"})])
# Get the resources
resources = {
Cores: 1,
SDRAM: region_utils.sizeof_regions([sys_region, keys_region],
None)
}
# Create the vertex
v = self.connection_vertices[transmission_params] = \
Vertex(self._label, get_application("rx"), resources)
self._sys_regions[v] = sys_region
self._key_regions[v] = keys_region
# Return the netlist specification
return netlistspec(list(self.connection_vertices.values()),
load_function=self.load_to_machine)
def __init__(self,
in_slice,
out_slice,
resources=dict(),
transmission_parameter_slices=list()):
super(ParallelFilterSlice, self).__init__(get_application("filter"),
resources)
# Store the slices
self.in_slice = in_slice
self.out_slice = out_slice
# Store which signal parameter slices we contain
self.transmission_params = list()
out_set = set(
range(out_slice.start or 0, out_slice.stop or 0, out_slice.step
or 1))
for transmission_params, outs in transmission_parameter_slices:
# If there is an intersection between the outs and the set of outs
# we're responsible for then store transmission parameters.
if out_set & outs:
self.transmission_params.append(transmission_params)
def __init__(self, label, column_slice, output_slice, transform_region,
output_keys, output_slices, machine_timestep, filter_region,
filter_routing_region):
"""Allocate a portion of the overall matrix to a single processing
core.
Parameters
----------
column_slice : :py:class:`slice`
Columns of the transform matrix managed by the group of vertices of
which we are a member.
output_slice : :py:class:`slice`
Slice of the rows of the transform matrix that will be applied by
this processing core.
transform_region : MatrixRegion
output_keys : [BitField, ...]
Keys transmitted by filter.
output_slices : [(TransmissionParameters, set), ...]
Pairs of transmission parameters and sets containing the row
indices of the transform matrix corresponding to the transmission
parameters.
"""
# Check that the output slice is safe
assert (output_slice.start is not None
and output_slice.stop is not None
and (output_slice.step is None or output_slice.step == 1))
# Store information about the slices of the for which matrix we're
# responsible.
self.output_slice = output_slice
self.column_slice = column_slice
# Store which signal parameter slices we contain
self.transmission_params = set()
out_set = set(range(output_slice.start, output_slice.stop))
for transmission_params, outs in output_slices:
# If there is an intersection between the outs and the set of outs
# we're responsible for then store transmission parameters.
if out_set & outs:
self.transmission_params.add(transmission_params)
# Construct the regions
self.regions = {
Regions.system:
SystemRegion(column_slice, output_slice, machine_timestep),
Regions.transform:
transform_region,
Regions.keys:
regions.KeyspacesRegion(
output_keys,
fields=[regions.KeyField(dict(cluster="cluster"))],
partitioned_by_atom=True),
Regions.input_filters:
filter_region,
Regions.input_routing:
filter_routing_region,
}
# Construct the region arguments
w = self.column_slice.stop - self.column_slice.start
self.region_arguments = {
Regions.transform: Args(vertex_slice=self.output_slice),
Regions.keys: Args(vertex_slice=self.output_slice),
Regions.system: Args(), # No arguments
Regions.input_filters: Args(filter_width=w), # No arguments
Regions.input_routing: Args(), # No arguments
}
# Determine the resource requirements and find the correct application
sdram_usage = regions.utils.sizeof_regions_named(
self.regions, self.region_arguments)
super(FilterCore, self).__init__(label=self._label,
application=get_application("filter"),
resources={
Cores: 1,
SDRAM: sdram_usage
})
def __init__(self, column_slice, output_slice,
transform_region, output_keys, output_slices,
machine_timestep, filter_region, filter_routing_region):
"""Allocate a portion of the overall matrix to a single processing
core.
Parameters
----------
column_slice : :py:class:`slice`
Columns of the transform matrix managed by the group of vertices of
which we are a member.
output_slice : :py:class:`slice`
Slice of the rows of the transform matrix that will be applied by
this processing core.
transform_region : MatrixRegion
output_keys : [BitField, ...]
Keys transmitted by filter.
output_slices : [(TransmissionParameters, set), ...]
Pairs of transmission parameters and sets containing the row
indices of the transform matrix corresponding to the transmission
parameters.
"""
# Check that the output slice is safe
assert (output_slice.start is not None and
output_slice.stop is not None and
(output_slice.step is None or output_slice.step == 1)
)
# Store information about the slices of the for which matrix we're
# responsible.
self.output_slice = output_slice
self.column_slice = column_slice
# Store which signal parameter slices we contain
self.transmission_params = set()
out_set = set(range(output_slice.start, output_slice.stop))
for transmission_params, outs in output_slices:
# If there is an intersection between the outs and the set of outs
# we're responsible for then store transmission parameters.
if out_set & outs:
self.transmission_params.add(transmission_params)
# Construct the regions
self.regions = {
Regions.system: SystemRegion(column_slice, output_slice,
machine_timestep),
Regions.transform: transform_region,
Regions.keys: regions.KeyspacesRegion(
output_keys,
fields=[regions.KeyField(dict(cluster="cluster"))],
partitioned_by_atom=True
),
Regions.input_filters: filter_region,
Regions.input_routing: filter_routing_region,
}
# Construct the region arguments
w = self.column_slice.stop - self.column_slice.start
self.region_arguments = {
Regions.transform: Args(vertex_slice=self.output_slice),
Regions.keys: Args(vertex_slice=self.output_slice),
Regions.system: Args(), # No arguments
Regions.input_filters: Args(filter_width=w), # No arguments
Regions.input_routing: Args(), # No arguments
}
# Determine the resource requirements and find the correct application
sdram_usage = regions.utils.sizeof_regions_named(
self.regions, self.region_arguments
)
super(FilterCore, self).__init__(
application=get_application("filter"),
resources={Cores: 1, SDRAM: sdram_usage}
)
def make_vertices(self, model, n_steps): # TODO remove n_steps
"""Construct the data which can be loaded into the memory of a
SpiNNaker machine.
"""
# Build encoders, gain and bias regions
params = model.params[self.ensemble]
# Convert the encoders combined with the gain to S1615 before creating
# the region.
encoders_with_gain = params.scaled_encoders
self.encoders_region = regions.MatrixRegion(
tp.np_to_fix(encoders_with_gain),
sliced_dimension=regions.MatrixPartitioning.rows
)
# Combine the direct input with the bias before converting to S1615 and
# creating the region.
bias_with_di = params.bias + np.dot(encoders_with_gain,
self.direct_input)
assert bias_with_di.ndim == 1
self.bias_region = regions.MatrixRegion(
tp.np_to_fix(bias_with_di),
sliced_dimension=regions.MatrixPartitioning.rows
)
# Convert the gains to S1615 before creating the region
self.gain_region = regions.MatrixRegion(
tp.np_to_fix(params.gain),
sliced_dimension=regions.MatrixPartitioning.rows
)
# Extract all the filters from the incoming connections
incoming = model.get_signals_connections_to_object(self)
self.input_filters, self.input_filter_routing = make_filter_regions(
incoming[InputPort.standard], model.dt, True,
model.keyspaces.filter_routing_tag, width=self.ensemble.size_in
)
self.inhib_filters, self.inhib_filter_routing = make_filter_regions(
incoming[EnsembleInputPort.global_inhibition], model.dt, True,
model.keyspaces.filter_routing_tag, width=1
)
self.mod_filters, self.mod_filter_routing = make_filter_regions(
{}, model.dt, True, model.keyspaces.filter_routing_tag
)
# Extract all the decoders for the outgoing connections and build the
# regions for the decoders and the regions for the output keys.
outgoing = model.get_signals_connections_from_object(self)
decoders, output_keys = \
get_decoders_and_keys(model, outgoing[OutputPort.standard], True)
size_out = decoders.shape[1]
# TODO: Include learnt decoders
self.pes_region = PESRegion()
self.decoders_region = regions.MatrixRegion(
tp.np_to_fix(decoders / model.dt),
sliced_dimension=regions.MatrixPartitioning.rows
)
self.output_keys_region = regions.KeyspacesRegion(
output_keys, fields=[regions.KeyField({'cluster': 'cluster'})]
)
# Create the recording regions for locally situated probes
self.spike_region = None
self.probe_spikes = False
self.voltage_region = None
self.probe_voltages = False
for probe in self.local_probes:
# For each probe determine which regions and flags should be set
if probe.attr in ("output", "spikes"):
# If spikes are being probed then ensure that the flag is set
# and a region exists.
if not self.probe_spikes:
self.spike_region = SpikeRegion(n_steps)
self.probe_spikes = True
elif probe.attr in ("voltage"):
# If voltages are being probed then ensure that the flag is set
# and a region exists.
if not self.probe_voltages:
self.voltage_region = VoltageRegion(n_steps)
self.probe_voltages = True
# If profiling is enabled
num_profiler_samples = 0
if getconfig(model.config, self.ensemble, "profile", False):
# Try and get number of samples from config
num_profiler_samples = getconfig(model.config, self.ensemble,
"profile_num_samples")
# If it's not specified, calculate sensible default
if num_profiler_samples is None:
num_profiler_samples =\
len(EnsembleLIF.profiler_tag_names) * n_steps * 2
# Create profiler region
self.profiler_region = regions.Profiler(num_profiler_samples)
# Create the regions list
self.regions = [
SystemRegion(self.ensemble.size_in,
size_out,
model.machine_timestep,
self.ensemble.neuron_type.tau_ref,
self.ensemble.neuron_type.tau_rc,
model.dt,
self.probe_spikes,
self.probe_voltages,
num_profiler_samples
),
self.bias_region,
self.encoders_region,
self.decoders_region,
self.output_keys_region,
self.input_filters,
self.input_filter_routing,
self.inhib_filters,
self.inhib_filter_routing,
self.gain_region,
self.mod_filters,
self.mod_filter_routing,
self.pes_region,
self.profiler_region,
self.spike_region,
self.voltage_region,
]
# Partition the ensemble and get a list of vertices to load to the
# machine. We can expect to be DTCM or CPU bound, so the SDRAM bound
# can be quite lax to allow for lots of data probing.
# TODO: Include other DTCM usage
def cpu_usage(sl):
"""Calculate the CPU usage (in cycles) based on the number of
neurons and the size_in and size_out of the ensemble.
The equation and coefficients are taken from: "An Efficient
SpiNNaker Implementation of the NEF", Mundy, Knight, Stewart and
Furber [IJCNN 2015]
"""
n_neurons = (sl.stop - sl.start)
return (245 + 43*self.ensemble.size_in + 100 + 702*size_out +
188 + 69*n_neurons + 13*n_neurons*self.ensemble.size_in)
self.vertices = list()
sdram_constraint = partition.Constraint(8*2**20) # Max 8MiB
dtcm_constraint = partition.Constraint(64*2**10, .75) # 75% of 64KiB
cpu_constraint = partition.Constraint(200000, .8) # 80% of 200k cycles
constraints = {
sdram_constraint: lambda s: regions.utils.sizeof_regions(
self.regions, s),
# **HACK** don't include last three regions in DTCM estimate
# (profiler and spike recording)
dtcm_constraint: lambda s: regions.utils.sizeof_regions(
self.regions[:-3], s) + 5*(s.stop - s.start),
cpu_constraint: cpu_usage,
}
app_name = (
"ensemble_profiled" if num_profiler_samples > 0
else "ensemble"
)
for sl in partition.partition(slice(0, self.ensemble.n_neurons),
constraints):
resources = {
Cores: 1,
SDRAM: regions.utils.sizeof_regions(self.regions, sl),
}
vsl = VertexSlice(sl, get_application(app_name), resources)
self.vertices.append(vsl)
# Return the vertices and callback methods
return netlistspec(self.vertices, self.load_to_machine,
after_simulation_function=self.after_simulation)
