Specksim is a high performance Spiking-Convolutional Neural Network simulator which is written in C++ and bound to our backend library samna. It simulates the SNN completely event-based and is a good representation of how our DYNAP-CNN architecture hardware processes events. It is designed to help users who do not have access to our chips to test their networks to see how they would perform if they were deployed on the chip. Furthermore, the network architectures that normally could not have been deployed to our hardware due to memory constrains can be tested in specksim. Specksim package is completely bound to Python, which means that any and all of its features can be used easily from Python.


Specksim is implemented partly in sinabs and partly in samna. If this library is installed via pip both of these libraries will be present. Therefore, no additional packages are necessary.

Supported architecture#

The specksim simulator only supports sequential models. Typically the container module we use is torch.nn.Sequential, however nested modules are also supported.

Each weight layer, torch.nn.Conv2d, torch.nn.Linear should be followed by a spiking layer sinabs.layers.IAF, sinabs.layers.IAFSqueeze.

The output layer of the network has to be a spiking layer.

Supported layers#

Parameter layers#

This simulation supports two weight layer, namely torch.nn.Conv2d and torch.nn.Linear. Any other parameter layer will be discarded. Linear layer will be converted into a Conv2d layer keeping all its features. Biases are not supported. We currently do not have a way to simulate biases as they behave on our hardware.

Pooling layers#

The simulation supports two pooling layers, namely torch.nn.AvgPool2d and sinabs.layers.SumPool2d. Any other pooling layer will be discarded. AvgPool2d layer will be converted to a SumPool2d layer, and the weights of the following parameter layer will be scaled down based on the kernel size of the pooling layer. Padding is not supported. Stride is considered to be equal to the kernel size for simulating the on-chip behaviour.

Spiking layers#

Only sinabs.layers.IAF and sinabs.layers.IAFSqueeze layers are supported. LIF neurons are not supported.


Specksim expects events in the format of np.record arrays. The record array has to have the following 4 keys. x, y, t and p; where x and y are the coordinates, t stands for timestamps and p stands for polarity (or in the context of non DVS input channels). The four keys are converted in np.uint32 format. Output format which can be seen in the next cell can be used for the input format as well.

event_format = np.dtype([("x", np.uint32), ("y", np.uint32), ("t", np.uint32), ("p", np.uint32)])



import torch.nn as nn
from sinabs.from_torch import from_model
from sinabs.backend.dynapcnn.specksim import from_sequential

Define an artificial neural network#

ann = nn.Sequential(
    nn.Conv2d(1, 20, 5, 1, bias=False),
    nn.AvgPool2d(2, 2),
    nn.Conv2d(20, 32, 5, 1, bias=False),
    nn.AvgPool2d(2, 2),
    nn.Conv2d(32, 128, 3, 1, bias=False),
    nn.AvgPool2d(2, 2),
    nn.Linear(128, 500, bias=False),
    nn.Linear(500, 10, bias=False),

You can then load weights.

Convert to SNN using sinabs#

snn = from_model(ann, add_spiking_output=True, batch_size=1).spiking_model

Now that a sinabs spiking neural network is defined, we can convert this model to a SpecksimNetwork using the method from_sequential.

Convert sequential SNNs to SpecksimNetwork#

input_shape = (1, 28, 28) # MNIST shape
specksim_network_dynapcnn = from_sequential(snn, input_shape=input_shape)

Please note that the input shape of the network has to be passed explicitly.

Convert from sequential DynapcnnNetwork to SpecksimNetwork#

In order to do a more realistic simulation, the sequential SNN network can be quantized by converting it into a DynapcnnNetwork object, then the quantized DynapcnnNetwork can be converted into the SpecksimNetwork.

input_shape = (1, 28, 28) # MNIST shape
dynapcnn_network = DynapcnnNetwork(snn=snn, input_shape=input_shape, dvs_input=False, discretize=True)
# the dynapcnn_network weights are quantized as we passed discretize=True
specksim_network_dynapcnn = from_sequential(dynapcnn_network, input_shape=input_shape)

Send events to the simulated SNN#

Now that the network is created we can send events and see if we receive any.

x = 10 # in pixels
y = 10 # in pixels
t = 100_000 # typically in microseconds
p = 0
input_event = np.array([x,y,t,p], dtype=specksim_network.output_dtype)
output_event = specksim_network(input_event)

Monitoring hidden layers#

Hidden layers of the network can be monitored by adding monitors to certain spiking layers. Please note that only the spiking layers are allowed to be monitored. In order to use that we use the SpecksimNetwork.add_monitor(int) or in order to add monitors to multiple layers, we can use the following SpecksimNetwork.add_monitors(List[int]). For monitoring the Nth spiking layer, you can pass N. Please note that the indices start from 0.

output_event = specksim_network(input_event)

Now that the forward pass has been completed, we can read from the monitor we have added. We do so using SpecksimNetwork.read_monitor(int). In order to read from multiple monitors, you can use the following convenience methods. SpecksimNetowrk.read_monitors(List[int]) or SpecksimNetwork.read_all_monitors() methods. Indexing is handled in the same way as adding monitors.

intermadiate_layer_events: np.record = specksim_network.read_monitor(0)

Please note that after each forward pass the monitors are flushed.

Monitoring hidden layer states.#

Due to the event-based nature of the hardware and simulation, the state updates cannot be stored as there are too many changes to store in the memory. However, the states can be read at the end of each call with read_spiking_layer_states(int) method. If the argument is N then the states of Nth spiking layer will be received.

states: List[List[List[int]]] = specksim_network.read_spiking_layer_states(0)

Resetting states#

Specksim is mainly designed for benchmarking network performances in an event-driven way. In benchmarking we typically reset the spiking layer states. This can be achived by the following.


Drawbacks and possible questions#

No training#

This simulator is inference only and it does not support training.

No biases#

Biases for weight layers, namely torch.nn.Conv2d and torch.nn.Linear are not supported. The biases should be set to False explicitly in the network before conversion.

Breadth-first vs Depth-first#

Our DYNAP-CNN architecture is completely asynchronous. This means that for a sequential model, each event comes after the event that created it from the previous layer. That means the hardware processes the events in a depth-first manner. This simulation however processes events layer-by-layer. If there are multiple events received by a layer. That layer first finishes processing these events and then adds them to a queue, so the next layer can start their processing. This is done to make the simulation more efficient and faster. Furthermore, for models that were trained in a rate-based manner, the change in the processing scheme does not create too big of a difference.

TLDR; The chip processes events in depth-first manner, whereas due to implementation efficiency specksim processes events in breadth-first manner.


Our hardware with the DYNAP-CNN architecture are completely asynchronous. Therefore, event timestamps do not play any role in calculation in the chip itself. The output events however do have timestamps, which are handled by the development boards. The timestamps depend on the load on the cores and therefore in a simulation cannot be replicated with a lot of success. Therefore, in this simulation for output events we assign the timestamp of the input event that led to its creation.


It is not possible to reliably run spiking neural network in real-time using specksim. Although event-based processing is implemented completely in C++ for any complex architecture with a lot of parameters and fan-out there will be certain delays.

Try it yourself#

An example of running a converted SNN trained on MNIST in specksim can be found under examples/mnist/specksim_network.py