SENSIM:

Codebase for the SENSIM simulator for emulation of Spiking Neural Network on SENeCA. The simulator uses certain dependencies. The easiest way to recreate the environment and play with the tool is to use annaconda to create the environment using the tf2.yml file.

NOTE: This code is only experimental and therefore not comes with no liability (particularly in what relates to its quality or efficiency)

Setting up the environment with annaconda

• conda env create -f tf2.yml

Application Dataset Setup

For the purpose of experimentation the we use the MNIST Dataset and the PilotNet DataSet Default location of the dataset searched by the scripts in DNNs/<application>/dataset folder. SENSIM is hardcoded to work with PilotNet but the application can be modified in the appdefs.py and iodefs.py

General

• Dir libraries/ contains the SENSIM simulator code
• Dir gui/ contains the code for gui and is also the default location for snapshots collected during simulation
• Dir DNNs/ contains the DNN applications (dataset, model in keras, SNN, weights and model parameters)
• File top.py is the primary script to execute the simulator
• File startGui.py is the script used to take data from the simulation and execute the GUI
• File appdefs.py is the file where application layers get defined and manually mapped to SENSIM
• File iodefs.py is the where the input dataset to the application mapped to SENSIM is defined
• File libraries/core.py is where the functionality of a single neurosynaptic core execution is defined.
• File libraries/simulator.py is where the application is mapped to different neurosynaptic cores and is executed as a whole
• File libraries/parameters.py is where hardware, simulation, energy, timing and commucation parameters are defined
• File libraries/interconnect.py is where the interconnect framwork which connects the neurosynaptic core is defined
• File libraries/Queue.py is where the queueing framwork for every neurosynaptic core is defined.
• File libraries/DNN_layers.py is where the layer framwork for the DNN is defined.

Execution of scripts

The execution of the simulator is majorly defined in 2 phases. The simulation of a SNN or DNN application on SENSIM.

• python top.py

Visualization of the execution on a GUI

• python startGui.py

Experiments

A few experiments with PilotNet and SENSIM were conducted and placed under the DNNs/PilotNet/*_experiments folder

• Dir bitwidth_experiment varies the bitwidth / precision for Weights, Neuron Outputs and thresholds.
• Dir eval_experiment varies the neuron evaluation type during simulation.
• Dir NoC_experiment varies with the multicast and unicast of events from 1 layer to another.
• Dir parallel_speed_experiment varies the number of physical cores for neurosynaptic cores and NoC interconnect
• Dir SharedMem_experiment varies the Bandwidth from the external memory.
• Dir sparsity_experiment varies the sparsity by varying the thresholds for the layer.
• Dir NPE_experiment varies the number of Neural Processing Elements in the neurosynaptic core.
• Dir mapping_experiment varies different mapping for PilotNet.

PilotNet

Dataset download and preparation

To prepare the dataset:

$ cd DNNs/PilotNet/dataset
$ wget -t https://drive.google.com/file/d/1PZWa6H0i1PCH9zuYcIh5Ouk_p-9Gh58B/
$ unzip driving_dataset.zip -d .

We have provided a small dataset of 250 images for testing

$ unzip pilotnet_0_250.zip -d .

SNN Model and DAL layer

Training the PilotNet with DAL layer to extract sparsity is detailed under DNNs/PilotNet/PilotNet_Delta.py

NOTE: The SNN model is already trained and the weights and thresholds are already provided.

MNIST

Model and quantization

Training and quantization of MNIST is specified under DNNs/4layer_CONV_MNIST/

• 4layer_CONV_MNIST.py
• weight_quantization.py

NOTE: The model is already trained and weights are already provided for testing.