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Quick Start

Export your first snnTorch model to T1C-IR in under 5 minutes.

SDK Alternative

You can also use the unified SDK: from t1c import sdk which includes all packages.

Step 1: Define a Model​

import torch
import torch.nn as nn
import snntorch as snn

class SimpleSNN(nn.Module):
    def __init__(self):
        super().__init__()
        self.fc1 = nn.Linear(784, 128)
        self.lif1 = snn.Leaky(beta=0.9)
        self.fc2 = nn.Linear(128, 10)
        self.lif2 = snn.Leaky(beta=0.9)
    
    def forward(self, x, mem1=None, mem2=None):
        if mem1 is None:
            mem1 = self.lif1.init_leaky()
        if mem2 is None:
            mem2 = self.lif2.init_leaky()
        
        cur1 = self.fc1(x)
        spk1, mem1 = self.lif1(cur1, mem1)
        cur2 = self.fc2(spk1)
        spk2, mem2 = self.lif2(cur2, mem2)
        
        return spk2, mem1, mem2

model = SimpleSNN()

Step 2: Export to T1C-IR​

from t1c import bridge, ir

# Sample input (batch_size=1, features=784)
sample = torch.randn(1, 784)

# Export
graph = bridge.to_ir(model, sample)

print(f"Nodes: {list(graph.nodes.keys())}")
print(f"Edges: {graph.edges}")

Output:

Nodes: ['input', 'fc1', 'lif1', 'fc2', 'lif2', 'output']
Edges: [('input', 'fc1'), ('fc1', 'lif1'), ('lif1', 'fc2'), ('fc2', 'lif2'), ('lif2', 'output')]

Step 3: Save to File​

# Save as HDF5
ir.write('model.t1c', graph)

# Verify
print(f"Saved. Version: {ir.read_version('model.t1c')}")

Step 4: Visualize​

from t1c import viz

# Display in browser or Jupyter
viz.visualize(graph, title="SimpleSNN")

# Or export to HTML file
path = viz.export_html(graph, "model.html")
print(f"Visualization: {path}")

Step 5: Import and Verify​

# Load graph
loaded = ir.read('model.t1c')

# Convert to executable PyTorch module
executor = bridge.ir_to_torch(loaded, return_state=True)

# Run inference
x = torch.randn(1, 784)
state = {}

for t in range(10):  # 10 timesteps
    output, state = executor(x, state)
    print(f"t={t}: spikes={output.sum().item():.0f}")

Complete Example​

import torch
import torch.nn as nn
import snntorch as snn
from t1c import ir, bridge, viz

# 1. Model
class SimpleSNN(nn.Module):
    def __init__(self):
        super().__init__()
        self.fc1 = nn.Linear(784, 128)
        self.lif1 = snn.Leaky(beta=0.9)
        self.fc2 = nn.Linear(128, 10)
        self.lif2 = snn.Leaky(beta=0.9)
    
    def forward(self, x, mem1=None, mem2=None):
        if mem1 is None:
            mem1 = self.lif1.init_leaky()
        if mem2 is None:
            mem2 = self.lif2.init_leaky()
        cur1 = self.fc1(x)
        spk1, mem1 = self.lif1(cur1, mem1)
        cur2 = self.fc2(spk1)
        spk2, mem2 = self.lif2(cur2, mem2)
        return spk2, mem1, mem2

model = SimpleSNN()

# 2. Export
sample = torch.randn(1, 784)
graph = bridge.to_ir(model, sample)

# 3. Save
ir.write('model.t1c', graph)

# 4. Visualize
viz.visualize(graph)

# 5. Import and run
executor = bridge.ir_to_torch('model.t1c', return_state=True)
output, state = executor(torch.randn(1, 784), {})
print(f"Output shape: {output.shape}")

Interactive Tutorial​

For a hands-on introduction with step-by-step explanations, see the T1C Tutorial Notebook.

What's Next​