metadata
license: mit
tags:
- neurocuda
- spiking-neural-network
- snn
- neuromorphic
- vision
- vision
- cifar-10
- strongcnn
- classification
- medium
pipeline_tag: image-classification
neurocuda/strongcnn-cifar10-snn ⚠️
7-layer CNN SNN for CIFAR-10. Gap of 6% with conversion + fine-tuning. Larger gap than ResNet-18 due to higher non-linearity. Good for studying conversion challenges in medium-depth networks.
Model Details
- Task: image-classification
- Dataset: CIFAR-10
- Architecture: StrongCNN (7-layer, BatchNorm, wider channels)
- Training: ANN → QCFS → IF + BPTT FT (conversion + fine-tune)
- Status: beta
Performance
- SNN Accuracy: 74.3% | Gap: +6.00% (within ANN)
- Parameters: 4,800,000 (18.3 MB)
- Timesteps: T=16
Usage
import neurocuda as nc
# Load the pre-converted spiking model
snn, info = nc.hub.load("neurocuda/strongcnn-cifar10-snn")
# The model is already spiking — binary IF/LIF spikes, stateful membrane
snn.eval()
# 4D input (single frame)
import torch
x = torch.randn(1, 2, 34, 34) # Adjust channels/size for your model
output = snn(x)
# 5D input (temporal — event cameras, video)
x5 = torch.randn(2, 16, 2, 34, 34) # (Batch, Timesteps, Channels, H, W)
output5 = snn(x5)
Hardware Compatibility
- Validated on: GPU, CPU
- NIR Export: Yes — deployable to Loihi 2, SpiNNaker, FPGA
Conversion Method
ANN → QCFS → IF + BPTT FT (conversion + fine-tune)
Citation
@software{neurocuda2026,
title = {NeuroCUDA: A PyTorch-to-Neuromorphic Compiler},
author = {Krishna Varma},
year = {2026},
url = {https://github.com/neurocuda/neurocuda}
}
Limitations
⚠️ Single seed result. Multi-seed verification pending.