July 4, 2026 · 18 min read

ResNet-18 SNN Conversion

ResNet-18 SNN conversion on CIFAR-10 is the benchmark that separates real ANN-to-SNN compilers from demo scripts. This tutorial trains the ANN baseline, converts at T=32, and targets the published 94.61% SNN accuracy.

Residual networks break naive resnet18 snn conversion because spikes from parallel branches must sum correctly at merge points. Many tools silently fail here. NeuroCUDA publishes multi-seed ResNet-18/CIFAR-10 numbers and bit-exact NIR round-trip for the residual graph in paper.pdf.

This tutorial is end-to-end code: data loaders, ANN training sketch, conversion, GPU compile, evaluation, sparsity, and NIR export. Assumes pip install neurocuda - see install guide if needed.

Target benchmarks

Complete tutorial script

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
from torch.utils.data import DataLoader, random_split
import neurocuda

DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
T = 32  # timesteps for ResNet-18 SNN conversion

# --- CIFAR-10 data ---
transform_train = transforms.Compose([
    transforms.RandomCrop(32, padding=4),
    transforms.RandomHorizontalFlip(),
    transforms.ToTensor(),
    transforms.Normalize((0.4914, 0.4822, 0.4465),
                         (0.2470, 0.2435, 0.2616)),
])
transform_test = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.4914, 0.4822, 0.4465),
                         (0.2470, 0.2435, 0.2616)),
])

train_full = torchvision.datasets.CIFAR10(
    root="./data", train=True, download=True, transform=transform_train)
test_set = torchvision.datasets.CIFAR10(
    root="./data", train=False, download=True, transform=transform_test)

cal_len = 5000
train_len = len(train_full) - cal_len
train_set, cal_set = random_split(train_full, [train_len, cal_len])

train_loader = DataLoader(train_set, batch_size=128, shuffle=True, num_workers=2)
cal_loader = DataLoader(cal_set, batch_size=64, shuffle=True, num_workers=2)
test_loader = DataLoader(test_set, batch_size=64, shuffle=False, num_workers=2)

Step 1: Train or load ResNet-18 ANN

model = torchvision.models.resnet18(weights=None, num_classes=10)
model = model.to(DEVICE)

# Option A: load pretrained checkpoint
# model.load_state_dict(torch.load("resnet18_cifar10.pth"))

# Option B: train ANN baseline (sketch)
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.1, momentum=0.9, weight_decay=5e-4)
scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=200)

model.train()
for epoch in range(200):
    for images, labels in train_loader:
        images, labels = images.to(DEVICE), labels.to(DEVICE)
        optimizer.zero_grad()
        loss = criterion(model(images), labels)
        loss.backward()
        optimizer.step()
    scheduler.step()

model.eval()
ann_acc = neurocuda.evaluate(model, test_loader, device=DEVICE)
print(f"ANN accuracy: {ann_acc:.2%}")

Target ANN accuracy: ~95.56%. If your baseline is lower, fix ANN training before ResNet-18 SNN conversion - conversion cannot recover information the ANN never learned.

Step 2: Convert ResNet-18 to SNN

snn = neurocuda.convert(
    model,
    cal_loader,
    timesteps=T,
    device=DEVICE
)

Inside convert(): QCFS calibrates ReLU activations per channel, BatchNorm folds into conv weights, IF neurons replace activations, BPTT fine-tunes with atan surrogate gradients. Residual add nodes are preserved for correct branch summation.

Step 3: Compile and evaluate

neurocuda.compile(snn, target="gpu")
snn_acc = neurocuda.evaluate(snn, test_loader, device=DEVICE)
sparsity = neurocuda.measure_sparsity(snn, test_loader, device=DEVICE)

print(f"SNN accuracy (T={T}): {snn_acc:.2%}")
print(f"Sparsity: {sparsity:.1%}")
print(f"Gap vs ANN: {(ann_acc - snn_acc)*100:.2f} pp")

Expect SNN accuracy near 94.61% and sparsity near 93.7% when ANN baseline matches published protocol. Variance across seeds is documented in the PDF.

Step 4: Cross-backend validation

neurocuda.compile(snn, target="cpu")
cpu_acc = neurocuda.evaluate(snn, test_loader, device="cpu")
print(f"CPU SNN accuracy: {cpu_acc:.2%}")

neurocuda.compile(snn, target="loihi2_sim")
loihi_acc = neurocuda.evaluate(snn, test_loader, device=DEVICE)
print(f"Loihi 2 sim accuracy: {loihi_acc:.2%}")

Loihi 2 sim validates against Intel's published IF equations, not physical silicon. Useful pre-hardware check when Lava is archived.

Step 5: NIR export for residual graph

neurocuda.to_nir(snn, "resnet18_cifar10_snn.nir")

ResNet's skip connections are why resnet18 snn conversion is a compiler stress test. NeuroCUDA's NIR executor handles residual graphs with 0.000000 max abs diff on round-trip per the technical report. See what is NIR.

Why T=32 timesteps

Each CIFAR-10 sample is presented as a constant input current over T steps. IF neurons integrate; deeper layers need enough steps for spikes to propagate through residual blocks. T=32 is the published default balancing accuracy and latency. Sweep T on your hardware:

for t in [8, 16, 32, 64]:
    snn_t = neurocuda.convert(model, cal_loader, timesteps=t, device=DEVICE)
    neurocuda.compile(snn_t, target="gpu")
    acc = neurocuda.evaluate(snn_t, test_loader, device=DEVICE)
    print(f"T={t}: {acc:.2%}")

ResNet-18 SNN conversion vs alternatives

Troubleshooting ResNet conversion

• Gap >3%: check ANN baseline, calibration set size, learning rate during BPTT (QCFS guide)
• ~10% accuracy: wrong normalization constants or T too low (debug guide)
• OOM on GPU: reduce batch_size in cal_loader and test_loader
• NIR import fails elsewhere: residual executor may differ; NeuroCUDA round-trip is the reference

After conversion: deployment paths

GPU validation is step one. Export NIR for Open Neuromorphic tooling, run Loihi 2 sim for equation confidence, or integrate via NeuroCUDA ROS2 for robotics. Physical Loihi requires INRC access separate from this tutorial.

Frequently asked questions

Run it: pip install neurocuda · Product page · PDF report