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Tutorial 40: One-Command FPGA Deployment

SC-NeuroCore deploys SNN models to FPGA in one command. Input: a trained model (NIR or PyTorch). Output: a complete synthesis project with Verilog, build scripts, and HDL library — ready for Yosys or Vivado.

Quick Start

# Deploy a NIR model to Lattice iCE40
sc-neurocore deploy model.nir --target ice40 -o build/

# Deploy to Xilinx Artix-7
sc-neurocore deploy model.nir --target artix7 -o build/

# Deploy a PyTorch state_dict
sc-neurocore deploy weights.pt --target zynq -o build/

What Gets Generated

build/
  sc_deploy_lif.sv       Generated neuron module (Q8.8 fixed-point)
  hdl/                   SC-NeuroCore Verilog library (19 modules)
    sc_lif_neuron.v      Q8.8 LIF core
    sc_bitstream_encoder.v  LFSR encoder
    sc_dense_layer_core.v   Dense layer pipeline
    sc_aer_encoder.v     Event-driven AER encoder
    sc_event_neuron.v    Event-triggered LIF
    ...
  Makefile               Yosys build script (ice40/ecp5)
  project.tcl            Vivado build script (artix7/zynq)
  README.md              Build instructions

Supported Targets

Target FPGA Tool Build command
ice40 Lattice iCE40 HX8K Yosys + nextpnr make synth
ecp5 Lattice ECP5-85K Yosys + nextpnr make synth
artix7 Xilinx Artix-7 100T Vivado vivado -mode batch -source project.tcl
zynq Xilinx Zynq 7020 Vivado vivado -mode batch -source project.tcl

Pipeline Stages

[1/5] Load model (NIR graph or PyTorch state_dict)
[2/5] Quantize weights to Q8.8 fixed-point
[3/5] Generate SystemVerilog neuron module
[4/5] Copy 19 HDL library modules
[5/5] Generate target-specific project files

From NIR

Any model exported to NIR (from Norse, snnTorch, SpikingJelly, etc.) can be deployed:

# Export from SpikingJelly
from spikingjelly.activation_based.nir_exchange import export_to_nir
graph = export_to_nir(model, torch.randn(1, n_input), dt=1e-4)
nir.write("model.nir", graph)

# Deploy to FPGA
sc-neurocore deploy model.nir --target artix7 --dt 1e-4 -o build/

From PyTorch

Save the model's state_dict (not the full model):

torch.save(model.state_dict(), "weights.pt")

sc-neurocore deploy weights.pt --target ice40 --T 256 -o build/

The deploy command reconstructs the model architecture from weight shapes and converts to an SNN using the conversion engine.

Bitstream Length

The --T flag sets the SC bitstream length (default 256):

sc-neurocore deploy model.nir --target ice40 --T 512 -o build/

Longer bitstreams give higher precision at the cost of more clock cycles. See Tutorial 19 for the precision-latency tradeoff.

Further Reading