2. Hardware acceleration

Norse is built on top of PyTorch which has excellent support for hardware acceleration. This article will cover how Norse can be accelerated with GPUs through the CUDA platform.

Since Norse is using PyTorch primitives, it is worth familiarising yourself with the use of GPUs in PyTorch. Here is a tutorial describing how to use GPUs with the to method in PyTorch: https://pytorch.org/tutorials/beginner/nn_tutorial.html#using-your-gpu

2.1. Accelerating Norse models with .to

To accelerate neuron models in Norse, one simply has to call the .to method on models and data:

from norse.torch.module.lif import LIFCell
LIFCell(10, 20).to('cuda')
import torch
torch.randn(100, 20).to('cuda')

2.2. Accelerating Norse nested models

It might be necessary to sometimes build your own nested torch.nn modules. We recommend that you adhere to the PyTorch torch.nn.Module idiosyncrasy, where you register possible nested tensors as either Parameters or Buffers .

If things are setup correctly, it should be simple to move models and tensors to the GPU (the example is a simplified version of our MNIST task example):

class LIFConvNet(torch.nn.Module):
    def __init__(
        super(LIFConvNet, self).__init__()
        self.constant_current_encoder = ConstantCurrentLIFEncoder(seq_length=seq_length)
        self.input_features = input_features
        self.rsnn = ConvNet4(method=model)
        self.seq_length = seq_length

    def forward(self, x):
        batch_size = x.shape[0]
        x = self.constant_current_encoder(
            x.view(-1, self.input_features)
        x = x.reshape(self.seq_length, batch_size, 1, 28, 28)
        voltages = self.rsnn(x)
        m, _ = torch.max(voltages, 0)
        log_p_y = torch.nn.functional.log_softmax(m, dim=1)
        return log_p_y

We can now initialise the model and move it to the correct device:

def main(args):
    device = ...
    input_features = ...
    seq_length = ...
    model = LIFConvNet(input_features, seq_length).to(device)