Skip to content

Product Overview

SC-NeuroCore is a stochastic-computing and neuromorphic hardware co-design toolkit. It helps teams move from numerical neuron and network experiments to auditable implementation artefacts: stochastic bitstreams, fixed-point representations, optional accelerated simulation, generated RTL, synthesis reports, and readiness evidence.

Plain-language summary

Most neural-network software optimises floating-point training and inference. SC-NeuroCore focuses on a different question: when can neural computation be represented as probability, spike timing, or stochastic bitstreams, and when can that representation be moved toward energy-aware hardware?

The project gives researchers and engineers one place to explore:

  • stochastic encoders and bitstream arithmetic;
  • spiking neuron and synapse models;
  • network simulation and training paths;
  • hardware export and co-simulation workflows;
  • benchmark and parity evidence;
  • evidence categories for industrial readiness.

Core workflow

  1. Model a neuron, synapse, network, or stochastic operator in Python.
  2. Validate numerical behaviour with module-specific tests, parity artefacts, or benchmark harnesses.
  3. Select an execution path: pure Python, optional Rust engine, optional training backend, or hardware-generation route.
  4. Export evidence: JSON benchmark results, generated RTL, synthesis reports, notebook evidence, or readiness assessments.
  5. Promote only claims that are backed by committed artefacts.

What a completed workflow produces

SC-NeuroCore is most useful when the output is not just a plot or a trained model, but an evidence packet that another engineer can inspect:

  • a deterministic Python or Rust reference result;
  • a quantisation or precision manifest that states the numerical contract;
  • generated RTL or deployment metadata when the hardware path is used;
  • benchmark or synthesis artefacts with command and environment context;
  • a documented evidence boundary that states what the artefact does not prove.

This is the practical bridge from research code to industrial diligence. A lab can use the same workflow to explore a biological model, an FPGA engineer can audit the emitted arithmetic, and a commercial reviewer can see which claims are ready, which are local-only, and which remain open evidence gaps.

Why stochastic computing matters

Stochastic computing represents values as probabilities over bitstreams. That can trade exact arithmetic for compact logic, fault tolerance, and hardware-friendly approximate computation. In neuromorphic systems, this aligns naturally with spikes, rates, stochastic synapses, and noisy physical substrates.

SC-NeuroCore treats stochasticity as a measurable engineering parameter rather than a demo effect. The documentation and benchmark policy require error bounds, parity checks, or committed artefacts before a result becomes a public claim.

What the repository contains

Layer Purpose
Public Python package Stable entry points for simulation, encoding, layers, selected hardware helpers, and documented APIs.
Optional Rust engine Acceleration for selected hot paths when the local environment provides the engine.
Training and interop extras PyTorch, NIR, JAX, CuPy, quantum, and Studio paths installed only when requested.
HDL and hardware evidence Verilog/SystemVerilog generation, primitive RTL, synthesis reports, and deployment guides.
Research notebooks Executable walkthroughs and evidence notebooks for selected workflows.
Polyglot research mirrors Julia, Go, Mojo, and Rust counterparts for selected kernels and comparison benchmarks.

Evidence boundary

SC-NeuroCore is a broad research and engineering stack, not a blanket certification claim. The public documentation distinguishes:

  • stable package surfaces from source-only research modules;
  • measured benchmark artefacts from planned comparison gaps;
  • synthesis/resource evidence from power or energy evidence;
  • readiness assessment from regulated deployment approval;
  • optional dependency paths from default installation guarantees.

Where to start