Neurocuda Cnn Nmnist Snn

Performance

• SNN Accuracy: 99.88% ± 0.02% | Gap: -0.18% (BETTER than ANN)
• Sparsity: 91.7% ± 0.5%
• Parameters: 147,466 (576 KB)
• Timesteps: T=16

Usage

import neurocuda as nc

# Load the pre-converted spiking model
snn, info = nc.hub.load("neurocuda/cnn-nmnist-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, Loihi 2 simulator
• NIR Export: Yes — deployable to Loihi 2, SpiNNaker, FPGA

Conversion Method

ANN → CS-QCFS → IF + BPTT FT (conversion)

Citation

@software{neurocuda2026,
  title    = {NeuroCUDA: A PyTorch-to-Neuromorphic Compiler},
  author   = {Krishna Varma},
  year     = {2026},
  url      = {https://github.com/neurocuda/neurocuda}
}

Limitations

This is a converted spiking neural network. Accuracy was measured on the full test set with ≥3 seeds (where noted). Performance may vary on different hardware backends. See the NeuroCUDA README for detailed benchmarking methodology.

--- license: mit tags: - neurocuda - spiking-neural-network - snn - neuromorphic - vision - vision - event-camera - n-mnist - classification - robotics - flagship pipeline_tag: image-classification --- # neurocuda/cnn-nmnist-snn ✅ Event-camera object classification SNN. Beats the original ANN by 0.18%. 92% sparse — only 8% of neurons fire. Best-in-class conversion. Our flagship model. ## Model Details - **Task:** event-camera-vision - **Dataset:** N-MNIST - **Architecture:** 3-layer CNN (2 input channels, 34×34) - **Training:** ANN → CS-QCFS → IF + BPTT FT (conversion) - **Status:** production ## Performance - **SNN Accuracy:** 99.88% ± 0.02% | **Gap:** -0.18% (BETTER than ANN) - **Sparsity:** 91.7% ± 0.5% - **Parameters:** 147,466 (576 KB) - **Timesteps:** T=16 ## Usage ```python import neurocuda as nc # Load the pre-converted spiking model snn, info = nc.hub.load("neurocuda/cnn-nmnist-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, Loihi 2 simulator - **NIR Export:** Yes — deployable to Loihi 2, SpiNNaker, FPGA ## Conversion Method ANN → CS-QCFS → IF + BPTT FT (conversion) ## Citation ```bibtex @software{neurocuda2026, title = {NeuroCUDA: A PyTorch-to-Neuromorphic Compiler}, author = {Krishna Varma}, year = {2026}, url = {https://github.com/neurocuda/neurocuda} } ``` ## Limitations This is a converted spiking neural network. Accuracy was measured on the full test set with ≥3 seeds (where noted). Performance may vary on different hardware backends. See the NeuroCUDA README for detailed benchmarking methodology.