d803bfe2b1
git-subtree-dir: vendor/ruvector git-subtree-split: b64c21726f2bb37286d9ee36a7869fef60cc6900
80 lines
2.5 KiB
Markdown
80 lines
2.5 KiB
Markdown
# exo-backend-classical
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Classical compute backend for the EXO-AI cognitive substrate with SIMD
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acceleration. Implements the `SubstrateBackend` trait from `exo-core` on
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standard CPU hardware, optimised for throughput and energy efficiency.
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## Features
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- **SIMD-accelerated vector operations** -- uses platform SIMD intrinsics
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(SSE4.2, AVX2, NEON) for fast dot products, cosine similarity, and
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element-wise transforms.
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- **Dither quantization integration** -- applies stochastic dithered
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quantization to compress activations while preserving gradient signal.
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- **Thermodynamic layer (thermorust)** -- wraps every compute step with
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Landauer energy accounting so the substrate can track real
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thermodynamic cost.
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- **Domain bridge with Thompson sampling** -- routes cross-domain
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queries to the most promising transfer path using Thompson sampling
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over historical success rates.
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- **Transfer orchestrator** -- coordinates end-to-end knowledge
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transfers across domains.
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- **5-phase cross-domain transfer pipeline** -- executes transfers
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through assess, align, project, adapt, and validate phases for
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reliable domain migration.
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## Quick Start
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Add the dependency to your `Cargo.toml`:
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```toml
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[dependencies]
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exo-backend-classical = "0.1"
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```
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Basic usage:
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```rust
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use exo_backend_classical::ClassicalBackend;
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use exo_core::SubstrateBackend;
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let backend = ClassicalBackend::new()
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.with_simd(true)
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.with_dither_quantization(8); // 8-bit dithered
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// Run a forward pass
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let output = backend.forward(&input_tensor)?;
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// Check thermodynamic cost
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println!("Energy: {:.4} kT", backend.energy_cost());
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// Cross-domain transfer (5-phase pipeline)
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let result = backend.transfer("vision", "language", &payload)?;
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println!("Transfer score: {:.4}", result.quality);
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```
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## Crate Layout
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| Module | Purpose |
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|-------------|----------------------------------------------|
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| `simd` | Platform-specific SIMD kernels |
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| `quantize` | Dither quantization and de-quantization |
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| `thermo` | Landauer energy tracking (thermorust) |
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| `bridge` | Domain bridge with Thompson sampling |
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| `transfer` | 5-phase cross-domain transfer orchestrator |
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## Requirements
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- Rust 1.78+
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- Depends on `exo-core`
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- Optional: AVX2-capable CPU for best SIMD performance
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## Links
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- [GitHub](https://github.com/ruvnet/ruvector)
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- [EXO-AI Documentation](https://github.com/ruvnet/ruvector/tree/main/examples/exo-ai-2025)
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## License
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MIT OR Apache-2.0
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