Neurocuda Mlp Mnist Snn

Performance

• SNN Accuracy: 97.4% | Gap: +0.40% (within ANN)
• Parameters: 269,322 (1052 KB)
• Timesteps: T=16

Usage

import neurocuda as nc

# Load the pre-converted spiking model
snn, info = nc.hub.load("neurocuda/mlp-mnist-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 (direct conversion, no FT)

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 - digit - mnist - classification - quickstart - beginner pipeline_tag: image-classification --- # neurocuda/mlp-mnist-snn ✅ Simple MLP SNN for MNIST digit classification. The classic beginner example — great for learning the conversion pipeline. Converts in under 1 minute on CPU. ## Model Details - **Task:** digit-classification - **Dataset:** MNIST - **Architecture:** 3-layer MLP (784→256→256→10) - **Training:** ANN → QCFS → IF (direct conversion, no FT) - **Status:** production ## Performance - **SNN Accuracy:** 97.4% | **Gap:** +0.40% (within ANN) - **Parameters:** 269,322 (1052 KB) - **Timesteps:** T=16 ## Usage ```python import neurocuda as nc # Load the pre-converted spiking model snn, info = nc.hub.load("neurocuda/mlp-mnist-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 (direct conversion, no FT) ## 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.