spikingjelly 0.0.0.0.3

A deep learning framework for SNNs built on PyTorch.

SpikingJelly

English | 中文

SpikingJelly is an open-source deep learning framework for Spiking Neural Network (SNN) based on PyTorch.

The documentation of SpikingJelly is written in both English and Chinese: https://spikingjelly.readthedocs.io

Installation

Note that SpikingJelly is based on PyTorch. Please make sure that you have installed PyTorch before you install SpikingJelly.

Install from PyPI：

pip install spikingjelly

Developers can download and install the latest version from source codes:

From GitHub:

git clone https://github.com/fangwei123456/spikingjelly.git
cd spikingjelly
python setup.py install

From OpenI：

git clone https://git.openi.org.cn/OpenI/spikingjelly.git
cd spikingjelly
python setup.py install

Build SNN In An Unprecedented Simple Way

SpikingJelly is user-friendly. Building SNN with SpikingJelly is as simple as building ANN in PyTorch:

class Net(nn.Module):
    def __init__(self, tau=100.0, v_threshold=1.0, v_reset=0.0):
        super().__init__()
        # Network structure, a simple two-layer fully connected network, each layer is followed by LIF neurons
        self.fc = nn.Sequential(
            nn.Flatten(),
            nn.Linear(28 * 28, 14 * 14, bias=False),
            neuron.LIFNode(tau=tau, v_threshold=v_threshold, v_reset=v_reset),
            nn.Linear(14 * 14, 10, bias=False),
            neuron.LIFNode(tau=tau, v_threshold=v_threshold, v_reset=v_reset)
        )

    def forward(self, x):
        return self.fc(x)

This simple network with a Poisson encoder can achieve 92% accuracy on MNIST test dataset. Read the tutorial of clock driven for more details. You can also run this code in Python terminal for training on classifying MNIST:

>>> import spikingjelly.clock_driven.examples.lif_fc_mnist as lif_fc_mnist
>>> lif_fc_mnist.main()

Read spikingjelly.clock_driven.examples to explore more advanced networks!

CUDA-Enhanced Neuron

SpikingJelly provides two versions of spiking neurons: user-friendly PyTorch version and high-speed CUDA version. The followed figure compares execution time of different LIF neurons:

Device Supports

• Nvidia GPU
• CPU

As simple as using PyTorch.

>>> net = nn.Sequential(nn.Flatten(), nn.Linear(28 * 28, 10, bias=False), neuron.LIFNode(tau=tau))
>>> net = net.to(device) # Can be CPU or CUDA devices

About

Multimedia Learning Group, Institute of Digital Media (NELVT), Peking University and Peng Cheng Laboratory are the main developers of SpikingJelly.

The list of developers can be found here.

Any contributions to SpikingJelly is welcome!