diff --git a/CSNN-Simulator/apps/MnistForSpiNNaker.cpp b/CSNN-Simulator/apps/MnistForSpiNNaker.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..65ed956e4557cea4032000306c5683030eb1c010
--- /dev/null
+++ b/CSNN-Simulator/apps/MnistForSpiNNaker.cpp
@@ -0,0 +1,78 @@
+#include "Experiment.h"
+#include "dataset/Mnist.h"
+#include "stdp/Simplified.h"
+#include "layer/Convolution.h"
+#include "Distribution.h"
+#include "execution/OptimizedLayerByLayer.h"
+
+int main(int argc, char** argv) {
+
+ std::string name = "mnist_spinn";
+ float do_prune = atof(argv[1]);
+ float do_reinforcement = atof(argv[2]);
+
+ if(do_prune == 1){
+ name = name + "_pruned";
+ }
+ if(do_reinforcement == 1){
+ name = name + "_reinforced";
+ }
+
+ Experiment<OptimizedLayerByLayer> experiment(argc, argv, name);
+
+ experiment.template input<LatencyCoding>();
+
+ const char* input_path_ptr = "../Datasets/MNIST/";
+
+ if(input_path_ptr == nullptr) {
+ throw std::runtime_error("Require to define INPUT_PATH variable");
+ }
+
+ std::string input_path(input_path_ptr);
+
+ experiment.template add_train<dataset::Mnist>(input_path+"train-images.idx3-ubyte", input_path+"train-labels.idx1-ubyte");
+ experiment.template add_test<dataset::Mnist>(input_path+"t10k-images.idx3-ubyte", input_path+"t10k-labels.idx1-ubyte");
+
+ float th_lr = 1.0f;
+ float t_obj = 0.50f;
+ float alpha = 0.05f;
+ float alpha_p= 0.01f;
+ float alpha_n= 0.005f;
+ float beta_p= 1.5f;
+ float beta_n= 2.5f;
+ float prune_max_threshold = 0.7f;
+
+ auto& fc1 = experiment.template push_layer<layer::Convolution>("fc1", 28, 28, 400);
+ fc1.template parameter<float>("annealing").set(0.95f);
+ fc1.template parameter<float>("min_th").set(1.0f);
+ fc1.template parameter<float>("t_obj").set(t_obj);
+ fc1.template parameter<float>("lr_th").set(th_lr);
+ fc1.template parameter<float>("doPrune").set(do_prune);
+ fc1.template parameter<float>("doReinforcement").set(do_reinforcement);
+ fc1.template parameter<float>("prune_max_threshold").set(prune_max_threshold);
+ fc1.template parameter<Tensor<float>>("w").template distribution<distribution::Uniform>(0.0, 1.0);
+ fc1.template parameter<Tensor<float>>("th").template distribution<distribution::Gaussian>(8.0, 0.1);
+ fc1.template parameter<STDP>("stdp").template set<stdp::Simplified>(alpha_p,alpha_n,beta_p,beta_n);
+
+ alpha = alpha*2;
+
+ auto& fc2 = experiment.template push_layer<layer::Convolution>("fc2", 1, 1, 1600);
+ fc2.template parameter<float>("annealing").set(0.95f);
+ fc2.template parameter<float>("min_th").set(1.0f);
+ fc2.template parameter<float>("t_obj").set(t_obj);
+ fc2.template parameter<float>("lr_th").set(th_lr);
+ fc2.template parameter<float>("doPrune").set(do_prune);
+ fc2.template parameter<float>("doReinforcement").set(do_reinforcement);
+ fc2.template parameter<float>("prune_max_threshold").set(prune_max_threshold);
+ fc2.template parameter<Tensor<float>>("w").template distribution<distribution::Uniform>(0.0, 1.0);
+ fc2.template parameter<Tensor<float>>("th").template distribution<distribution::Gaussian>(10.0, 0.1);
+ fc2.template parameter<STDP>("stdp").template set<stdp::Simplified>(alpha_p,alpha_n,beta_p,beta_n);
+
+ experiment.add_train_step(fc1, 25);
+ experiment.add_train_step(fc2, 25);
+
+ experiment.run(10000);
+
+ return experiment.wait();
+
+}
\ No newline at end of file
diff --git a/README.md b/README.md
index 44d2c1053eab314deb0e8ce1770479b1eb814671..16d49939ad761ef238ae6a8a405ace6f6e25b8a8 100644
--- a/README.md
+++ b/README.md
@@ -20,7 +20,7 @@ Run the following commands inside the CSNN folder:
Remember to build again if you change the source code.
### How to use CSNN
-Once the `make` command is finished, you should see binary files which represent each simulation.
+Once the `make` command is done, you should see binary files representing each simulation.
Run a simulation:
@@ -34,12 +34,20 @@ For example: to run MNIST simulation without compression and reinforcement:
./Mnist 0 0
-In the `apps` folder, you find the source code for each simulation where you can change the architecture, the network parameters, or activate the [layerwise compression](https://gitlab.univ-lille.fr/hammouda.elbez/progressive-layer-based-compression-for-convolutional-spiking-neural-network/-/blob/main/CSNN-Simulator/apps/Mnist.cpp#L21).
+In the `apps` folder, you will find the source code for each simulation, where you can change the architecture and the network parameters or activate the [layerwise compression](https://gitlab.univ-lille.fr/hammouda.elbez/progressive-layer-based-compression-for-convolutional-spiking-neural-network/-/blob/main/CSNN-Simulator/apps/Mnist.cpp#L21).
## Going from CSNN to SpiNNaker
+First, run the simulation of the two dense layers using CSNN:
+
+ ./MnistForSpiNNaker 0 0
+
+then
+
+ ./MnistForSpiNNaker 1 1
+
To transfer the learned weights from CSNN to SpiNNaker, we use the following command:
- ./Weight_extractor [binary file geenrated from a simulation] [name_layer]
+ ./Weight_extractor [binary file generated from a simulation] [name_layer]
For example:
diff --git a/SpiNNaker/README.MD b/SpiNNaker/README.MD
new file mode 100644
index 0000000000000000000000000000000000000000..5cd2309fb6ce4edfe3ae1a9f77b199b4efd2f9af
--- /dev/null
+++ b/SpiNNaker/README.MD
@@ -0,0 +1,18 @@
+## Files naming
+In order to properly use the provided scripts, make sure to rename the binary weights extracted from the csnn-simulator as follows:
+
+As an example, we have the next architecture with MNIST dataset:
+
+`Input(784) x Fc1(400) x Fc2(1600)`
+
+
+ weights_fc1_X:
+ Weights between Input and Fc1 layer
+ weights_fc2_X:
+ Weights between Fc1 and Fc2 layer
+
+ X = type of network [baseline or compressed]
+
+>`binary file` : are from the csnn-simulator (Weight Extractor), which <span style="color:red;">needs to be renamed by the user</span>
+
+>`.txt file` : represents the generated connections file for Spynnaker
\ No newline at end of file
diff --git a/SpiNNaker/SpiNNakerRun.ipynb b/SpiNNaker/SpiNNakerRun.ipynb
index b40d6a6aaa2e0fd7b3d868960cc4d8c8a1947ad8..8f63e6ce52ec0e12041c5f4edf2f6c527c9b27e9 100644
--- a/SpiNNaker/SpiNNakerRun.ipynb
+++ b/SpiNNaker/SpiNNakerRun.ipynb
@@ -30,18 +30,24 @@
"\n"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Initialization\n"
+ ]
+ },
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
- "# Initialization\n",
"\n",
"# Encoding parameters\n",
- "SMAPLES_INTERVAL=10#\n",
+ "SMAPLES_INTERVAL=10\n",
"TIME_STEP=1\n",
- "NUM_STEPS=20#20\n",
+ "NUM_STEPS=20\n",
"\n",
"# Simulator parameters\n",
"timestep=[1]\n",
@@ -61,6 +67,14 @@
"os.mkdir(f'{path}/{str(now)}') "
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Dataset and preparation\n",
+ "#### Passing data (Ex. mnistTest.data[0:10]: 10 digits from test set of MNIST dataset) to convert_to_latency_code_V4, to generate SpikeSourcArray for input population "
+ ]
+ },
{
"cell_type": "code",
"execution_count": 7,
@@ -75,7 +89,6 @@
}
],
"source": [
- "# Dataset and preparation\n",
"transform=transforms.Compose([\n",
" transforms.Resize((28,28)),\n",
" transforms.Grayscale(),\n",
@@ -90,6 +103,16 @@
"runtime =time"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Main loop\n",
+ "#### Will call the sim.run for different values (from range(5,10,5)) for **tau_refrac**\n",
+ "#### Also using other loops you can see the effects of other Simulator (number_of_neurons_per_core, time_step, timeScaleFactor) or neuron parameters\n",
+ "#### weights_files (generated from the CSNN binary weight files) is used for loading the text files as the connections between the popoulations"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -687,7 +710,6 @@
" if not os.path.exists(f'{path}/{str(now)}/{param_name}_{str(param_val)}/{str(time)}'): # 2\n",
" os.mkdir(f'{path}/{str(now)}/{param_name}_{str(param_val)}/{str(time)}')\n",
"\n",
- " total_mem=[]\n",
" spikes_count=[[] for _ in range(len(tests_name))]\n",
" for n_test, test_name in enumerate(tests_name):\n",
"\n",