SC-NeuroCore is a pioneering neuromorphic compiler that transforms high-level Python SNN definitions into bit-true hardware logic, achieving speeds up to 512x real-time on standard hardware. With its polymorphic engine, it supports a variety of advanced computational models, making it a powerful tool for the future of hardware-near real-time processing.
SC-NeuroCore v3.7 is the industry's first verified Rust-based neuromorphic compiler designed to convert high-level Python Spiking Neural Network (SNN) definitions into bit-true hardware logic. Running at an impressive 512x real-time speed on standard CPUs, SC-NeuroCore v3.7 enhances the stochastic kernel with a polymorphic engine that supports Hyper-Dimensional Computing (HDC), Vector Symbolic Architectures (VSA), Petri Nets, and fault-tolerant binary streams.
Key Features
- Performance: Achieve processing speeds that are significantly faster than traditional neuromorphic technologies.
- Polymorphic Engine: Versatile architecture supporting a wide range of applications in machine learning and AI.
- Bit-True Equivalence: Ensures hardware logic fidelity, verified against SystemVerilog hardware co-simulation, with 8/8 tests passed.
Quick Start Example
Install the package using pip:
pip install sc-neurocore-engine
Begin using the SC-NeuroCore engine:
import sc_neurocore_engine
print(sc_neurocore_engine.__version__) # Output: 3.7.0
Use Cases
- SNN Dense Forward Processing
from sc_neurocore_engine import DenseLayer layer = DenseLayer(n_inputs=784, n_neurons=128, length=1024) x = np.random.rand(784) y = layer.forward_numpy(x) # Single sample batch = np.random.rand(50, 784) Y = layer.forward_batch_numpy(batch) # 50 samples in one call - Hyper-Dimensional Computing (HDC)
from sc_neurocore_engine import HDCVector # Encode knowledge using 10,000-bit hypervectors - Stochastic Petri Net Simulation
from sc_neurocore_engine import PetriNetEngine engine = PetriNetEngine({...}) for _ in range(10): engine.step() print(engine.marking) # Token distribution - Fault-Tolerant Logic
from sc_neurocore_engine import BitStreamTensor t = BitStreamTensor.from_packed([...]) - IR Compilation to SystemVerilog
from sc_neurocore_engine import ScGraphBuilder b = ScGraphBuilder("my_net") sv_code = ir_emit_sv(b.build())
Performance Benchmarks (v3.7.0)
| Metric | Legacy Python | SC-NeuroCore v3.7 | Speedup |
|---|---|---|---|
| LIF Neuron Update | 12.9 ms | 0.025 ms | 512.4x |
| Dense Synaptic Layer | 64.0 ms | 0.380 ms | 168.0x |
| Bit-Stream Encoding | 51.0 ms | 0.342 ms | 149.3x |
| Inference Latency | ~2.5 ms | < 0.010 ms | > 250x |
Explore SC-NeuroCore for applications in industrial control, AI, biocomputing R&D, and more. Access detailed API documentation at anulum.github.io/sc-neurocore.
No comments yet.
Sign in to be the first to comment.