File size: 3,307 Bytes
e4cdd5f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 | # Neurocore SDK
Python SDK for the Catalyst N1 neuromorphic processor.
## Installation
```bash
pip install -e .
```
For GPU simulation (optional):
```bash
pip install torch # PyTorch with CUDA support
```
## Quick Start
```python
import neurocore as nc
# Build a network
net = nc.Network()
inp = net.population(100, params={'threshold': 1000, 'leak': 10}, label='input')
hid = net.population(50, params={'threshold': 1000, 'leak': 5}, label='hidden')
out = net.population(10, params={'threshold': 1000, 'leak': 5}, label='output')
net.connect(inp, hid, weight=500, probability=0.3)
net.connect(hid, out, weight=400, probability=0.5)
# Simulate
sim = nc.Simulator()
sim.deploy(net)
# Inject spikes and run
for t in range(100):
sim.inject(inp, neuron_ids=[0, 5, 10], current=1500)
sim.step()
# Analyze results
result = sim.get_result()
result.raster_plot(show=True)
```
## Backends
| Backend | Import | Description |
|---------|--------|-------------|
| `Simulator` | `neurocore.Simulator` | CPU reference simulator (LIF neurons) |
| `GpuSimulator` | `neurocore.GpuSimulator` | PyTorch CUDA accelerated (4-8x speedup at 4K+ neurons) |
| `Chip` | `neurocore.Chip` | UART interface to FPGA (Arty A7) |
| `F2Backend` | `neurocore.f2.F2Backend` | AWS F2 FPGA via PCIe MMIO |
All backends share the same `deploy(net)` / `step()` / `get_result()` API.
## Package Structure
```
neurocore/
__init__.py # Public API exports
network.py # Network, Population, Connection
compiler.py # Network -> hardware instructions
simulator.py # CPU LIF simulator
gpu_simulator.py # PyTorch GPU simulator
chip.py # UART FPGA backend
f2.py # AWS F2 PCIe backend
result.py # Spike recording and analysis
analysis.py # Raster plots, firing rates, ISI
topology.py # all_to_all, random, small_world, ring
microcode.py # Learning rule microcode compiler
constants.py # Hardware limits (WEIGHT_MIN/MAX, etc.)
exceptions.py # NeuroError, CompileError, etc.
```
## Benchmarks
```
benchmarks/
shd_train.py # Spiking Heidelberg Digits (surrogate gradient)
shd_deploy.py # SHD model quantization and deployment
shd_loader.py # SHD dataset loader (HDF5)
stress_test.py # SDK stress tests (saturation, stability, fan-out)
scaling_benchmark.py # Neuron count scaling performance
gpu_benchmark.py # CPU vs GPU simulator comparison
```
### SHD Benchmark
Train a spiking neural network on spoken digit classification:
```bash
# Download dataset (first run)
python benchmarks/shd_train.py --data-dir benchmarks/data/shd --epochs 200
# Evaluate quantization for hardware deployment
python benchmarks/shd_deploy.py --checkpoint benchmarks/shd_model.pt --data-dir benchmarks/data/shd
```
## Tests
```bash
pytest tests/ -v # 168 tests
pytest tests/ -v -k gpu # GPU tests only (requires CUDA)
```
## Hardware Requirements
- **CPU Simulator**: Python 3.9+, NumPy
- **GPU Simulator**: PyTorch 2.0+ with CUDA
- **Chip backend**: pyserial, FPGA with UART connection
- **F2 backend**: AWS F2 instance, fpga_mgmt library
|