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Neuromap SDK

Program neuromorphic chips with Python.

Neuromap is a Python SDK for building, training, quantizing, and exporting spiking neural networks (SNNs) for Neuromap neuromorphic hardware. It gives you a PyTorch-based workflow that goes from an untrained model to a portable .nmap hardware bundle.

Why Neuromap

  • Chip-aware networks. Define networks that match your target chip's topology, weight bit-width, and neuron parameters, so what you train is what you export.
  • Batteries-included training. A single Trainer class handles LR warmup, scheduling, gradient clipping, early stopping, and best-model checkpointing.
  • Hardware export. Quantize weights to the chip's DAC bit-width and package everything into a portable .nmap archive.
  • Streaming inference. Stateful chunk-by-chunk processing for real-time applications.

Install

pip install neuromap

The pipeline in one screen

from neuromap import Network, Trainer, Exporter, chips

# 1. Build a network matching the NeuroSoC-v1 chip
net = Network(chips.NEUROSOC_V1)
print(net.summary())

# 2. Train it (bring your own DataLoader)
trainer = Trainer(net, lr=1e-3, epochs=20)
history = trainer.fit(train_loader, val_loader)

# 3. Export a hardware bundle
Exporter(net).quantize().save("model.nmap")

Where to go next

  • Quickstart walks through the same pipeline step by step, with runnable snippets.
  • Concepts explains the four-stage model behind the SDK: chip spec, network, trainer, and exporter.
  • API Reference is the full class-by-class reference, generated from the source docstrings.