SynOpCounter
Contents
SynOpCounter#
SNNSynOpCounter#
- class sinabs.synopcounter.SNNSynOpCounter(model, dt=1.0)#
Counter for the synaptic operations emitted by all SpikingLayers in a spiking model. Note that this is automatically instantiated by from_torch and by Network if they are passed synops=True.
- Usage:
counter = SNNSynOpCounter(my_spiking_model)
output = my_spiking_model(input) # forward pass
synops_table = counter.get_synops()
- Parameters
model – Spiking model.
dt – the number of milliseconds corresponding to a time step in the simulation (default 1.0).
- get_synops() pandas.core.frame.DataFrame #
Method to compute a table of synaptic operations for the latest forward pass.
NOTE: this may not be accurate in presence of average pooling.
- Returns
- A Pandas DataFrame containing layer IDs and respectively, for the latest forward pass performed, their:
number of input spikes, fanout, synaptic operations, number of timesteps, total duration of simulation, number of synaptic operations per second.
- Return type
SynOps_dataframe
- get_total_power_use(j_per_synop=1e-11)#
Method to quickly get the total power use of the network, estimated over the latest forward pass.
- Parameters
j_per_synop – Energy use per synaptic operation, in joules. Default 1e-11 J.
Returns: estimated power in mW.
- get_total_synops(per_second=False) float #
Faster method for computing total synaptic operations without using Pandas.
NOTE: this may not be accurate in presence of average pooling.
- Parameters
per_second (bool, default False) – if True, gives synops per second instead of total synops in the last forward pass.
- Returns
the total synops in the network, based on the last forward pass.
- Return type
synops
SynOpCounter#
- class sinabs.synopcounter.SynOpCounter(modules, sum_activations=True)#
Counter for the synaptic operations emitted by all Neuromorphic ReLUs in an analog CNN model.
- Usage:
counter = SynOpCounter(MyTorchModel.modules(), sum_activations=True)
output = MyTorchModule(input) # forward pass
synop_count = counter()
- Parameters
modules – list of modules, e.g. MyTorchModel.modules()
sum_activations – If True (default), returns a single number of synops, otherwise a list of layer synops.