© 1998–2026 Miroslav Šotek. All rights reserved.
License: GNU AFFERO GENERAL PUBLIC LICENSE v3
Commercial Licensing: Available
# SC-NeuroCore
<p align="center">
<img src="docs/assets/sc_neurocore_header.png" width="1280" alt="SC-NeuroCore — Stochastic Computing & Neuromorphic Engine">
</p>
[](https://github.com/anulum/sc-neurocore/actions/workflows/ci.yml)
[](https://github.com/anulum/sc-neurocore/releases)
[](https://github.com/anulum/sc-neurocore)
[](https://anulum.github.io/sc-neurocore/)
[](https://www.gnu.org/licenses/agpl-3.0)
[](https://www.python.org/downloads/)
[](https://www.rust-lang.org/)
[](https://www.bestpractices.dev/projects/10362)
[](https://scorecard.dev/viewer/?uri=github.com/anulum/sc-neurocore)
[](https://reuse.software/)
**Version:** 3.8.2
SC-NeuroCore is a deterministic stochastic computing framework for
neuromorphic hardware design and edge-AI deployment. It provides bit-true
Python simulation (digital twin environment) that matches Verilog RTL
cycle-exactly, a high-performance Rust engine (512x real-time), GPU-accelerated
inference, and a tiered module system from production FPGA targets to
research prototyping.
## Quick Start
```bash
# Install from PyPI (core engine only — neurons, synapses, layers, HDL gen, compiler)
pip install sc-neurocore
# Or install with all research modules included
pip install sc-neurocore[full]
# GPU acceleration (requires CUDA)
pip install sc-neurocore[gpu]
```
### Development Setup
```bash
git clone https://github.com/anulum/sc-neurocore.git
cd sc-neurocore
pip install -e ".[dev]" # editable install with all dev tools
make preflight # verify setup (lint + 869 tests)
```
## Docker
The Docker image ships with the full Rust engine (512x real-time performance):
```bash
# Build
make docker-build
# or: docker build -f deploy/Dockerfile -t sc-neurocore:latest .
# Run interactive Python shell
make docker-run
# or: docker run --rm -it sc-neurocore:latest
# Smoke test via docker compose
docker compose -f deploy/docker-compose.yml up
```
Pre-built images are published to GHCR on every release:
```bash
docker pull ghcr.io/anulum/sc-neurocore:latest
docker run --rm -it ghcr.io/anulum/sc-neurocore:latest
```
## Performance Routing
Use explicit path selection for dense inference to avoid small-batch regressions:
- Single sample or micro-batch (1-4 samples): call `DenseLayer.forward_fast(...)`.
- Medium/large batch (>=10 samples): call `DenseLayer.forward_batch_numpy(...)`.
- Validation/reference path: use `DenseLayer.forward(...)` and compare to fast paths in tests.
For benchmark reports, always include batch size, bitstream length, seed policy, and CPU SIMD tier.
## Architecture
### Module Tiers
`pip install sc-neurocore` ships **Core + Simulation + Domain bridges** only.
Research and Frontier modules are available from source (`pip install -e ".[dev]"`).
| **Core** | neurons, synapses, layers, sources, utils, recorders, accel, compiler, hdl_gen, hardware, cli, exceptions | Yes | Production-ready. 98%+ coverage. |
| **Simulation** | hdc, solvers, transformers, learning, graphs, ensembles, export, pipeline, profiling, models, math, spatial, verification, security | Yes | Stable. Import explicitly. |
| **Domain bridges** | quantum (Qiskit/PennyLane), adapters/holonomic (JAX), scpn (Petri nets) | Yes | Requires `pip install sc-neurocore[quantum]` or `[jax]` |
| **Research** | robotics, physics, bio, optics, chaos, sleep, interfaces | No | Tested. Available from source. |
| **Frontier** | generative, world_model, analysis, audio, dashboard, viz, swarm | No | Experimental. Available from source. |
| **Speculative** | `research/` (eschaton, exotic, meta, post_silicon, transcendent) | No | Theoretical. See `research/README.md`. |
### Architecture Diagram
```mermaid
graph TD
subgraph "Python API (pip install sc-neurocore)"
A[BitstreamEncoder] --> B[SCDenseLayer / SCConv2DLayer]
B --> C[StochasticLIF / Izhikevich Neurons]
C --> D[STDP / R-STDP Synapses]
D --> E[BitstreamSpikeRecorder]
end
subgraph "Acceleration"
B --> F{Backend?}
F -->|CPU| G[NumPy / Numba SIMD]
F -->|GPU| H[CuPy CUDA]
F -->|Rust| I[sc_neurocore_engine<br/>512x real-time]
end
subgraph "Hardware Target"
I --> J[IR Compiler]
J --> K[SystemVerilog Emitter]
K --> L[Verilog RTL<br/>AXI-Lite + LIF Core]
L --> M[FPGA Bitstream<br/>Xilinx / Intel]
end
subgraph "Domain Bridges (optional)"
B --> N[SCPN Petri Nets]
B --> O[Quantum: Qiskit / PennyLane]
B --> P[HDC/VSA Symbolic Memory]
end
style A fill:#2d6a4f,color:#fff
style I fill:#b5651d,color:#fff
style L fill:#1a237e,color:#fff
style M fill:#4a148c,color:#fff
```
### Core API (28 symbols)
```python
from sc_neurocore import (
# Neurons
StochasticLIFNeuron, FixedPointLIFNeuron, FixedPointLFSR,
FixedPointBitstreamEncoder, HomeostaticLIFNeuron,
StochasticDendriticNeuron, SCIzhikevichNeuron,
# Synapses
BitstreamSynapse, BitstreamDotProduct,
StochasticSTDPSynapse, RewardModulatedSTDPSynapse,
# Layers
SCDenseLayer, SCConv2DLayer, SCLearningLayer,
VectorizedSCLayer, SCRecurrentLayer, MemristiveDenseLayer,
SCFusionLayer, StochasticAttention,
# Utilities
BitstreamEncoder, BitstreamAverager, RNG,
generate_bernoulli_bitstream, generate_sobol_bitstream,
bitstream_to_probability,
# Sources & Recorders
BitstreamCurrentSource, BitstreamSpikeRecorder,
)
```
### Hardware (Verilog RTL)
```
hdl/
sc_bitstream_encoder.v -- LFSR-based stochastic encoder (SEED_INIT param)
sc_bitstream_synapse.v -- AND-gate SC multiplier
sc_dotproduct_to_current.v -- Popcount -> fixed-point current
sc_lif_neuron.v -- Q8.8 leaky integrate-and-fire
sc_firing_rate_bank.v -- Spike rate estimator
sc_dense_layer_core.v -- Full dense layer pipeline (decorrelated seeds)
sc_neurocore_top.v -- AXI-Lite configuration wrapper
sc_axil_cfg.v -- AXI-Lite register file
tb_sc_lif_neuron.v -- Co-simulation testbench
```
### GPU Acceleration
```python
from sc_neurocore.accel import xp, HAS_CUPY, to_device, to_host
from sc_neurocore.accel.gpu_backend import gpu_vec_mac
# VectorizedSCLayer auto-detects GPU
layer = VectorizedSCLayer(n_inputs=32, n_neurons=64, length=1024)
output = layer.forward(input_values) # GPU if CuPy available, else CPU
```
## Hardware-Software Co-Simulation
The co-sim flow verifies bit-exact equivalence between the Python model and
Verilog RTL:
```bash
# 1. Generate stimuli + expected results (Python golden model)
python scripts/cosim_gen_and_check.py --generate
# 2. Run Verilog simulation (requires Icarus Verilog)
iverilog -o tb_lif hdl/sc_lif_neuron.v hdl/tb_sc_lif_neuron.v
vvp tb_lif
# 3. Compare results
python scripts/cosim_gen_and_check.py --check
```
### Reproducibility
Every GitHub Release includes:
- **sdist** — source distribution (`dist/*.tar.gz`)
- **SBOM** — CycloneDX software bill of materials (`sbom.json`)
- **Changelog extract** — release notes from `CHANGELOG.md`
Co-simulation traces are generated deterministically from fixed LFSR seeds.
To reproduce a published benchmark:
```bash
git checkout v3.8.2
pip install -e ".[dev]"
python benchmarks/benchmark_suite.py --markdown > BENCHMARKS.md
```
For Verilog co-sim trace reproduction, see `scripts/cosim_gen_and_check.py`
and the seed constants in `hdl/sc_bitstream_encoder.v`.
### Key Technical Details
- **LFSR**: 16-bit maximal-length, polynomial x^16+x^14+x^13+x^11+1, period 65535
- **Seed strategy**: Input encoders `0xACE1 + i*7`, weight encoders `0xBEEF + i*13`
- **Fixed-point**: Q8.8 (DATA_WIDTH=16, FRACTION=8), signed two's complement
- **Overflow**: Explicit bit-width masking via `_mask()` function
## Examples
Runnable scripts in `examples/`:
| `01_basic_sc_encoding.py` | Bernoulli & Sobol bitstream encoding/decoding |
| `02_sc_neuron_layer.py` | SCDenseLayer construction and forward pass |
| `03_ir_compile_demo.py` | IR graph building, verification, SystemVerilog emission (v3 Rust engine) |
| `04_vectorized_layer.py` | VectorizedSCLayer throughput benchmarking |
| `05_scpn_stack.py` | Full 7-layer SCPN consciousness stack with inter-layer coupling |
| `06_hdl_generation.py` | Verilog top-level generation from a network description |
| `07_ensemble_consensus.py` | Multi-agent ensemble orchestration and voting |
| `08_hdc_symbolic_query.py` | Hyper-Dimensional Computing symbolic memory (v3 Rust engine) |
| `09_safety_critical_logic.py` | Fault-tolerant Boolean logic with stochastic redundancy (v3 Rust engine) |
| `10_benchmark_report.py` | Head-to-head v2/v3 benchmark suite (v3 Rust engine) |
| `11_sc_training_demo.py` | Surrogate-gradient training of an SC dense layer (v3 Rust engine) |
```bash
PYTHONPATH=src:bridge python examples/01_basic_sc_encoding.py
```
Examples marked **(v3 Rust engine)** require the compiled `sc_neurocore_engine` wheel.
All other examples run with the pure-Python `sc_neurocore` package.
## CI/CD
12 GitHub Actions workflows (`.github/workflows/`), all SHA-pinned:
| **ci.yml** | Lint (black + ruff + bandit) + Test (Python 3.10/3.11/3.12, coverage ≥ 98%) + Build |
| **v3-engine.yml** | Rust engine `cargo test` + `cargo clippy` |
| **v3-wheels.yml** | Cross-platform wheels (Linux, macOS, Windows × Python 3.10–3.12) |
| **docker.yml** | Build & push Docker image to GHCR on release tags |
| **docs.yml** | MkDocs → GitHub Pages |
| **publish.yml** | PyPI OIDC trusted publisher on release |
| **release.yml** | sdist + changelog extraction → GitHub Release |
| **benchmark.yml** | Performance regression tracking |
| **codeql.yml** | CodeQL security analysis (weekly + on push) |
| **scorecard.yml** | OpenSSF Scorecard |
| **pre-commit.yml** | Pre-commit hook validation |
| **stale.yml** | Auto-label and close stale issues |
## Benchmarks
Run the benchmark suite:
```bash
python benchmarks/benchmark_suite.py # quick mode
python benchmarks/benchmark_suite.py --full # thorough (10x)
python benchmarks/benchmark_suite.py --markdown # output BENCHMARKS.md
```
Sample results (CPU, quick mode):
| LFSR step | 2.25 Mstep/s |
| Bitstream encoder | 1.88 Mstep/s |
| LIF neuron step | 1.15 Mstep/s |
| vec_and (1024 words) | 45.67 Gbit/s |
| gpu_vec_mac (64x32x16w) | 6.15 GOP/s |
## Documentation
**Live site**: [anulum.github.io/sc-neurocore](https://anulum.github.io/sc-neurocore/)
- [Getting Started](docs/guides/getting-started.md) -- Installation & quickstart
- [API Reference](docs/api/API_REFERENCE.md) -- Python package API
- [Rust Engine API](https://anulum.github.io/sc-neurocore/rust-api/sc_neurocore_engine/) -- Rust engine docs
- [Hardware Guide](docs/hardware/HARDWARE_GUIDE.md) -- FPGA deployment workflow
- [Architecture](docs/architecture/architecture.md) -- Package architecture
- [Benchmarks](docs/benchmarks/BENCHMARKS.md) -- Performance measurements
- [CHANGELOG.md](CHANGELOG.md) -- Version history
Build docs locally:
```bash
pip install mkdocs mkdocs-material mkdocstrings[python]
mkdocs serve
```
## Install Extras
```bash
pip install sc-neurocore # core engine only (neurons, layers, compiler, HDL gen)
pip install sc-neurocore[gpu] # + CuPy CUDA acceleration
pip install sc-neurocore[jax] # + JAX backend for holonomic adapters
pip install sc-neurocore[quantum] # + Qiskit + PennyLane quantum bridges
pip install sc-neurocore[full] # + networkx, onnx, qiskit, pennylane
pip install sc-neurocore[research] # + networkx, onnx, torch
```
For development (includes all modules + research/frontier code from source):
```bash
pip install -e ".[dev]" # editable install with pytest, mypy, black, hypothesis
```
Pinned dependency files for reproducible environments:
```bash
pip install -r requirements.txt # runtime only
pip install -r requirements-dev.txt # runtime + dev tools
```
## Community
- [GitHub Discussions](https://github.com/anulum/sc-neurocore/discussions) — questions, ideas, show & tell
- [Issue Tracker](https://github.com/anulum/sc-neurocore/issues) — bug reports and feature requests
- [Contributing Guide](CONTRIBUTING.md) — how to set up, test, and submit PRs
## License
SC-NeuroCore is dual-licensed:
- **Open Source**: [GNU Affero General Public License v3.0](LICENSE) (AGPLv3)
- **Commercial**: Proprietary license available for integration into closed-source products
For commercial licensing enquiries, contact [protoscience@anulum.li](mailto:protoscience@anulum.li).