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Speeding up snips by communicating less often #26

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Speeding up snips by communicating less often #26

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eaebf8e
First draft of documentation.
tbekolay Jul 26, 2018
cfe42bc
Fix n_steps in shape demo
drasmuss Aug 13, 2018
d14606b
Remove second confusing VTH_MAX
hunse Aug 16, 2018
ea5482b
Add MNIST pseudo-conv net example
drasmuss Jul 3, 2018
da7be19
Match learning rates to Nengo
tbekolay Aug 17, 2018
8c23740
Moved spike generator into snips
Aug 10, 2018
e77a8dc
Added snip_io_steps to control communication rate
Aug 12, 2018
03a6d42
Only move spike generators to be on-chip for loihi
Aug 12, 2018
a9eff59
PEP8 fixes
Aug 12, 2018
ab549fe
Flake8 fixes
Aug 12, 2018
d4e0a15
fixup! Added snip_io_steps to control communication rate
hunse Sep 5, 2018
d89dfa3
Added code comments for clarity
Sep 10, 2018
b598de6
Changed default snip_io_steps to 1 (from 10)
Sep 10, 2018
a2cac8a
fixup! Added code comments for clarity
Sep 10, 2018
68303cc
fixup! Moved spike generator into snips
Sep 12, 2018
5380800
Added test for interneurons on or off host
tcstewar Sep 12, 2018
a71e353
Removed repetition of input synapse
Sep 12, 2018
e64de2e
fixup! Removed repetition of input synapse
tcstewar Sep 12, 2018
688f8c0
fixup! Removed repetition of input synapse
tcstewar Sep 25, 2018
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18 changes: 10 additions & 8 deletions nengo_loihi/simulator.py
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Original file line number Diff line number Diff line change
Expand Up @@ -161,6 +161,13 @@ def __init__(self, network, dt=0.001, seed=None, model=None, # noqa: C901
self.precompute = precompute
self.snip_io_steps = snip_io_steps

if target is None:
try:
import nxsdk
target = 'loihi'
except ImportError:
target = 'sim'
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Not related to what's being done here, but it might be nice to have some sort of output (print statements? logger statements?) that indicate which target has been chosen. I would probably do it even in the case that the user has explicitly specified target (so after this if block).

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Possibly, although I'm not sure what the best sort of message would be. I'd definitely leave it for a separate PR, though.

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Pretty sure we already have these logging statements, they're just not raised here.


self.chip2host_sent_steps = 0 # how many timesteps have been sent
if network is not None:
nengo.rc.set("decoder_cache", "enabled", "False")
Expand All @@ -180,8 +187,10 @@ def __init__(self, network, dt=0.001, seed=None, model=None, # noqa: C901
progress_bar=False)
else:
# we need online communication
spiking_interneurons_on_host = target != 'loihi'
host, chip, h2c, c2h_params, c2h = splitter.split(
network, INTER_RATE, INTER_N)
network, INTER_RATE, INTER_N,
spiking_interneurons_on_host=spiking_interneurons_on_host)
network = chip
self.chip2host_receivers = c2h
self.host2chip_senders = h2c
Expand All @@ -208,13 +217,6 @@ def __init__(self, network, dt=0.001, seed=None, model=None, # noqa: C901
self.loihi = None
self.simulator = None

if target is None:
try:
import nxsdk
target = 'loihi'
except ImportError:
target = 'sim'

if target == 'simreal':
logger.info("Using real-valued simulator")
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E.g. here

self.simulator = self.model.get_simulator(seed=seed)
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40 changes: 29 additions & 11 deletions nengo_loihi/splitter.py
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Original file line number Diff line number Diff line change
Expand Up @@ -91,7 +91,8 @@ def __init__(self, dimensions):
super(ChipReceiveNeurons, self).__init__(dimensions, dimensions)


def split(model, inter_rate, inter_n): # noqa: C901
def split(model, inter_rate, inter_n,
spiking_interneurons_on_host=True): # noqa: C901
"""Split a model into code running on the host and on-chip"""

logger.info("Splitting model into host and chip parts")
Expand Down Expand Up @@ -180,7 +181,6 @@ def split(model, inter_rate, inter_n): # noqa: C901
with host:
max_rate = inter_rate * inter_n
assert max_rate <= 1000
# TODO: move the above check elsewhere

# scale the input spikes based on the radius of the
# target ensemble
Expand All @@ -189,15 +189,33 @@ def split(model, inter_rate, inter_n): # noqa: C901
else:
scaling = 1.0

logger.debug("Creating HostSendNode for %s", c)
send = HostSendNode(dim)
nengo.Connection(c.pre, send,
function=c.function,
solver=c.solver,
eval_points=c.eval_points,
scale_eval_points=c.scale_eval_points,
synapse=c.synapse,
transform=c.transform * scaling)
if spiking_interneurons_on_host:
logger.debug("Creating NIF ensemble for %s", c)
ens = nengo.Ensemble(
2 * dim, dim, neuron_type=NIF(tau_ref=0.0),
encoders=np.vstack([np.eye(dim), -np.eye(dim)]),
max_rates=[max_rate] * dim + [max_rate] * dim,
intercepts=[-1] * dim + [-1] * dim)
logger.debug("Creating HostSendNode for %s", c)
send = HostSendNode(dim * 2)
nengo.Connection(c.pre, ens,
function=c.function,
solver=c.solver,
eval_points=c.eval_points,
scale_eval_points=c.scale_eval_points,
synapse=None,
transform=c.transform * scaling)
nengo.Connection(ens.neurons, send, synapse=None)
else:
logger.debug("Creating HostSendNode for %s", c)
send = HostSendNode(dim)
nengo.Connection(c.pre, send,
function=c.function,
solver=c.solver,
eval_points=c.eval_points,
scale_eval_points=c.scale_eval_points,
synapse=c.synapse,
transform=c.transform * scaling)
host2chip_senders[send] = receive
elif pre_onchip and not post_onchip:
dim = c.size_out
Expand Down