Spiking Neural Network Performance Tool

This module compares SNN [algorithms] to their default/Neumann implementations. The user can specify an SNN and "normal" algorithm which take as input a networkx graph, and compute some graph property as output. The output of the SNN is then compared to the "normal" algorithm as "ground truth", in terms of:

Score*: How many results the SNN algorithm computed correctly (from a set of input graphs).
Runtime
Energy Complexity (nr of spikes)
Space Complexity (nr of neurons)
Connectivity (nr of synapses)
Radiation Robustness

*In theory, the score should always be 100% for the SNN, as it should be an exact SNN implementation of the ground truth algorithm. This comparison is mainly relevant for the additions of brain adaptation and simulated radiation.

Example

Below is an example of the SNN behaviour of the MDSA algorithm without adaptation, without radiation, on a (non-triangular) input graph of 5 nodes.

The green dots are when the neurons spike, non-spiking neurons are yellow.

Brain adaptation

For each SNN algorithm that the user specifies, the user can also specify a form of brain-inspired adaptation. This serves to increase the robustness of the SNN against radiation effects. The [brain-adaptation] can be called from a separate pip package called: snnadaptation.

Radiation

A basic form of [radiation] effects is modelled on the SNNs. For example, radiation is modelled as yielding permanent activity termination for random neurons.

It is noted that the accuracy of the modelling of the neuronal effects induced by the radiation is a function of the underlying hardware platforms. For example, on the Intel Loihi chips, the memory/routing and computations are somewhat intertwined from what I understood. This would suggest that radiation effects may yield errors that prevent a computation being executed at all, instead of a computation being corrupted, if for example a memory address is corrupted. (If that memory, for example, were to orchestrate some group of neurons to do something, but instead orchestrates an inactive set of neurons to perform some computation). In such cases, "neuronal- & synaptic" adaptation could be the best in the world, but nothing would happen with it if the neurons don't get the right input/send the output to the wrong place.

In hardware platforms where neurons and synapses have a more physical implementation on chip, the adaptation may be more effective to increase the radiation robustness.

Backends

Since the effectiveness of the adaptation mechanisms, in terms of radiation robustness, is a function of neuromorphic hardware platform, multiple [backends] are supported. These backends also allow for different neuronal and synaptic models. Currently the following backends are supported:

A self-made networkx SNN simulator (LIF-neurons)
Lava-nc simulator v0.5.0 (LIF-neurons)

Algorithms

Different SNN implementations may use different encoding schemes, such as sparse coding, population coding and/or rate coding. In population coding, adaptation may be realised in the form of larger populations, whereas in rate coding, adaptation may be realised through varying the spike-rate. This implies that different algorithms may benefit from different types of adaptation. Hence, an overview is included of the implemented SNN algorithms and their respective compatibilities with adaptation and radiation implementations:

Algorithm	Encoding	Adaptation	Radiation
Minimum Dominating Set Approximation	Sparse	Redundancy	Neuron Death

Minimum Dominating Set Approximation

This is an implementation of the distributed algorithm presented by Alipour et al.

Input: Non-triangle, planar Networkx graph. (Non triangle means there should not be any 3 nodes that are all connected with each other (forming a triangle)). Planar means that if you lay-out the graph on a piece of paper, no lines intersect (that you can roll it out on a 2D plane).
Output: A set of nodes that form a dominating set in the graph.

Description: The algorithm basically consists of k rounds, where you can choose k based on how accurate you want the approximation to be, more rounds (generally) means more accuracy. At the start each node i gets 1 random number r_i. This is kept constant throughout the entire algorithm. Then for the first round:

Each node i computes how many neighbours (degree) d_i it has.
Then it adds r_i+d_i=w_i. In all consecutive rounds:
Each node i "computes" which neighbour has the highest weight w_j, and gives that node 1 mark/point. Then each node i has some mark/score m_i. Next, the weight w_i=r_i+m_i is computed (again) and the next round starts. This last round is repeated until k rounds are completed. At the end, the nodes with a non-zero mark/score m_i are selected to form the dominating set.

Experiment Stages

The experiment generates some input graphs, the SNN algorithm, a copied SNN with some form of adaptation, and two copies with radiation (one with-/out adaptation). Then it simulates those SNNs for "as long as it takes" (=implicit in the algorithm specification), and computes the results of these 4 SNNs based on the "ground truth" Neumann/default algorithm.

This experiment is executed in 4 stages:

Input: Experiment configuration. Which consists of: SubInput: Run configuration within an experiment. Stage 1: Create networkx graphs that will be propagated. Stage 2: Create propagated networkx graphs (at least one per timestep). Stage 3: Visaualisation of the networkx graphs over time. Stage 4: Post-processed performance data of algorithm and adaptation mechanism. Stage 5: Create box plot with network performances.

Running Experiment

First satisfy the prerequisites:

pip install snncompare
pip install https://github.com/a-t-0/lava/archive/refs/tags/v0.5.1.tar.gz
ulimit -n 800000

You can run the experiment (stage 1,2,4) with command:

python -m src.snncompare -e mdsa_long_no_overwrite -j1 -j2 -j4

This generates the graphs from the default experiment configurations, and outputs the graphs in json format to the results/ directory, and outputs the graph behaviour to: latex/Images/graphs/.

Additional Options

You can run the experiment (stage 1,2,4) in reverse (from small to large graphs) with command:

python -m src.snncompare -e mdsa_long_no_overwrite -j1 -j2 -j4 -rev

You can run a single run_config with:

python -m src.snncompare -e mdsa_long_no_overwrite -j1 -j2 -j4 -r run_config_file_name

Typical run (deletes pre-existing results):

python -m src.snncompare -e neuron_death -j1 -j2 -j4 -j5 -s2 -rev
python -m src.snncompare -e quicktest -j1 -j2 -j4 -j5 -s2 -rev -dr
python -m src.snncompare -e qt0 -j1 -j2 -j4 -j5 -s2 -rev
python -m src.snncompare -e basic_results -j1 -j2 -j4 -j5 -s2 -rev -dr
python -m src.snncompare -e minimal_results -j1 -j2 -j4 -j5 -s2 -rev -dr
python -m src.snncompare -e test_population -j1 -j2 -j4 -j5 -s2 -rev -dr
python -m src.snncompare -e complexity -j1 -j2 -j4 -j5 -s2 -rev
python -m src.snncompare -e complexity -j5
python -m src.snncompare -e change_u -j5

Debugging:

python -m src.snncompare -e qt0 --export-failure-modes --show-failure-modes -rev
python -m src.snncompare -e qt0 -j1 -j2 -j4 -j5 --export-failure-modes \
--show-failure-modes -rev
python -m src.snncompare -e qt5 -j1 -j2 -j4 -j5 --export-failure-modes \
--show-failure-modes -rev


python -m src.snncompare -e qt3 -j1 -j2 -j4 -j5 --export-failure-modes \
--show-failure-modes -rev
python -m src.snncompare -e qt3 -j5 --export-failure-modes \
--show-failure-modes -rev
python -m src.snncompare -e qt3 --export-failure-modes \
--show-failure-modes -rev
python -m src.snncompare -e qt0 -j1 -j2 -j4 -si -sgt snn_algo_graph \
-p 8060 -rui 24ccfad34b33e780304bf588bdc6cb4e1a093b94e8b9f4c98a272b96cf5b20c8

python -m src.snncompare -e qt0 -j1 -j2 -j4 -si -sgt adapted_snn_graph \
-p 8060 -rui 05592d66394f93e51f1aec5d02ff6f8bd33f46c374101cb1c2c28eba5f4463c9
python -m src.snncompare -e qt0 -j1 -j2 -j4 -si -sgt rad_adapted_snn_graph \
-p 8060 -rui 8c8518173c3fecc6d495b9f7ecc83d5d8516cab2c1bfa388eee5c7770798b7ee

python -m src.snncompare -e qt0 -j1 -j2 -j4 -si -sgt adapted_snn_graph -p \
8060 -rui db2aaeda8a45710d0bbba18efeedfaf983e21111aa362cf7d1c77716cd882056
python -m src.snncompare -e qt0 -j1 -j2 -j4 -si -sgt rad_adapted_snn_graph \
-p 8060 -rui db2aaeda8a45710d0bbba18efeedfaf983e21111aa362cf7d1c77716cd882056

Demo

Debug 2 runs, in separate console:

python -m src.snncompare -e live_demo_adaptation -j1 -j2 -j4 -j5 -rev  -si -sgt \
 snn_algo_graph -p 8000
python -m src.snncompare -e live_demo_adaptation -j1 -j2 -j4 -j5 -rev  -si -sgt \
 rad_adapted_snn_graph -p 8003

Full visualisation:

python -m src.snncompare -e v0 -j1 -j2 -j4 -j5 -rev -dr -di -si -sgt \
 rad_adapted_snn_graph -p 8000
python -m src.snncompare -e v0 -j1 -j2 -j4 -j5 -rev -dr -di -si -sgt \
 rad_snn_graph -p 8000

For more info, run:

python -m src.snncompare --help

And run tests with:

python -m pytest

Run specific test:

python -m pytest tests/synapse_excitation/test_synapse_exitation.py

or to see live output, on any tests filenames containing substring: results:

python -m pytest tests/sparse/MDSA/test_snn_results_with_adaptation.py --capture=tee-sys

Developers

Improve the project using:

mkdir -p ~/git/snn
mkdir ~/git/snn/.vscode
mkdir -p ~/bin
cd ~/git/snn

git clone https://github.com/a-t-0/snnadaptation.git
git clone https://github.com/a-t-0/snnalgorithms.git
git clone https://github.com/a-t-0/snnbackends.git
git clone https://github.com/a-t-0/snnradiation.git
git clone https://github.com/a-t-0/snncompare.git
git clone https://gitlab.socsci.ru.nl/Akke.Toeter/simsnn.git

cd snncompare
conda env create --file environment.yml
git checkout excitatory-radiation
chmod +x snnrb
./snnrb --branch excitatory-radiation
./snnrb --rebuild

cp snncompare/.vscode/settings.json .vscode/settings.json

Then you can commit/update your work across all repos at once with:

snnrb -c "Some commit."

Test Coverage

Developers can use:

conda env create --file environment.yml
conda activate snncompare
ulimit -n 800000
python -m pytest

Currently the test coverage is 65%. For type checking:

mypy --disallow-untyped-calls --disallow-untyped-defs tests/export_results/performed_stage/test_performed_stage_TTFF.py

Releasing pip package update

To udate the Python pip package, one can first satisfy the following requirements:

pip install --upgrade pip setuptools wheel
pip install twine

Followed by updating the package with:

python3 setup.py sdist bdist_wheel
python -m twine upload dist/\*

Developer pip install

mkdir -p ~/bin
cp snn_rebuild.sh ~/.local/bin/snnrb
chmod +x ~/bin/snnrb

Updating

Build the pip package with:

pip install --upgrade pip setuptools wheel
pip install twine

Install the pip package locally with:

rm -r dist
rm -r build
python3 setup.py sdist bdist_wheel
pip install -e .

Upload the pip package to the world with:

rm -r dist
rm -r build
python3 setup.py sdist bdist_wheel
python3 -m twine upload dist/\*