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SC-NeuroCore
The first verified Rust-based neuromorphic compiler for real-time applications.
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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.

Description

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

  1. 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
    
  2. Hyper-Dimensional Computing (HDC)
    from sc_neurocore_engine import HDCVector
    # Encode knowledge using 10,000-bit hypervectors
    
  3. Stochastic Petri Net Simulation
    from sc_neurocore_engine import PetriNetEngine
    engine = PetriNetEngine({...})
    for _ in range(10):
        engine.step()
    
    print(engine.marking)  # Token distribution
    
  4. Fault-Tolerant Logic
    from sc_neurocore_engine import BitStreamTensor
    t = BitStreamTensor.from_packed([...])
    
  5. 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)

MetricLegacy PythonSC-NeuroCore v3.7Speedup
LIF Neuron Update12.9 ms0.025 ms512.4x
Dense Synaptic Layer64.0 ms0.380 ms168.0x
Bit-Stream Encoding51.0 ms0.342 ms149.3x
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.

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