This tutorial walks through the NeuroBench harness components and run setup using the Google Speech Commands (GSC) keyword classification task. Each of the other benchmarks also has a notebook walkthrough, which can be found in the NeuroBench examples directory. The GSC task is presented here for its simplicity.

Benchmark Task: The task is to classify keywords from the GSC dataset test split, after training using the train and val splits.

First we will import the relevant libraries. These include the dataset, pre- and post-processors, model wrapper, and benchmark object.

import torch
# import the dataloader
from torch.utils.data import DataLoader

# import the dataset, preprocessors and postprocessors you want to use
from neurobench.datasets import SpeechCommands
from neurobench.preprocessing import S2SPreProcessor
from neurobench.postprocessing import choose_max_count

# import the NeuroBench wrapper to wrap the snnTorch model
from neurobench.models import SNNTorchModel
# import the benchmark class
from neurobench.benchmarks import Benchmark

For this tutorial, we will make use of a simple feedforward SNN, written using snnTorch.

from torch import nn
import snntorch as snn
from snntorch import surrogate

beta = 0.9
spike_grad = surrogate.fast_sigmoid()
net = nn.Sequential(
    nn.Linear(20, 256),
    snn.Leaky(beta=beta, spike_grad=spike_grad, init_hidden=True),
    nn.Linear(256, 256),
    snn.Leaky(beta=beta, spike_grad=spike_grad, init_hidden=True),
    nn.Linear(256, 256),
    snn.Leaky(beta=beta, spike_grad=spike_grad, init_hidden=True),
    nn.Linear(256, 35),
    snn.Leaky(beta=beta, spike_grad=spike_grad, init_hidden=True, output=True),

To get started, we will load our desired dataset in a dataloader:

test_set = SpeechCommands(path="data/speech_commands/", subset="testing")

test_set_loader = DataLoader(test_set, batch_size=500, shuffle=True)

Here, we are loading a pre-trained model. The model is wrapped in the SNNTorchModel wrapper, which includes boilerplate inference code and interfaces with the top-level Benchmark class.


# Wrap our net in the SNNTorchModel wrapper
model = SNNTorchModel(net)

Specify any pre-processors and post-processors you want to use. These will be applied to your data before feeding into the model, and to the output spikes respectively. Here, we are using the Speech2Spikes pre-processor to convert the keyword audio data to spikes, and the choose_max_count post-processor which returns a classification based on the neuron with the greatest number of spikes.

preprocessors = [S2SPreProcessor()]
postprocessors = [choose_max_count]

Next specify the metrics which you want to calculate. The metrics include static metrics, which are computed before any model inference, and workload metrics, which show inference results.

  • Footprint: Bytes used to store the model parameters and buffers.

  • Connection sparsity: Proportion of zero weights in the model.

  • Classification accuracy: Accuracy of keyword predictions.

  • Activation sparsity: Proportion of zero activations, averaged over all neurons, timesteps, and samples.

  • Synaptic operations: Number of weight-activation operations, averaged over keyword samples.

    • Effective MACs: Number of non-zero multiply-accumulate synops, where the activations are not spikes with values -1 or 1.

    • Effective ACs: Number of non-zero accumulate synops, where the activations are -1 or 1 only.

    • Dense: Total zero and non-zero synops.

static_metrics = ["footprint", "connection_sparsity"]
workload_metrics = ["classification_accuracy", "activation_sparsity", "synaptic_operations"]

Next, we instantiate the benchmark. We pass the model, the dataloader, the preprocessors, the postprocessor and the list of the static and data metrics which we want to measure:

benchmark = Benchmark(model, test_set_loader,
                      preprocessors, postprocessors, [static_metrics, workload_metrics])

Now, let’s run the benchmark and print our results!

results = benchmark.run()

Expected output: {‘footprint’: 583900, ‘connection_sparsity’: 0.0, ‘classification_accuracy’: 0.8484325295196562, ‘activation_sparsity’: 0.9675956131759854, ‘synaptic_operations’: {‘Effective_MACs’: 0.0, ‘Effective_ACs’: 3556689.9895502045, ‘Dense’: 29336955.0}}