exo-backend-classical

Classical compute backend for the EXO-AI cognitive substrate with SIMD acceleration. Implements the SubstrateBackend trait from exo-core on standard CPU hardware, optimised for throughput and energy efficiency.

Features

• SIMD-accelerated vector operations -- uses platform SIMD intrinsics (SSE4.2, AVX2, NEON) for fast dot products, cosine similarity, and element-wise transforms.
• Dither quantization integration -- applies stochastic dithered quantization to compress activations while preserving gradient signal.
• Thermodynamic layer (thermorust) -- wraps every compute step with Landauer energy accounting so the substrate can track real thermodynamic cost.
• Domain bridge with Thompson sampling -- routes cross-domain queries to the most promising transfer path using Thompson sampling over historical success rates.
• Transfer orchestrator -- coordinates end-to-end knowledge transfers across domains.
• 5-phase cross-domain transfer pipeline -- executes transfers through assess, align, project, adapt, and validate phases for reliable domain migration.

Quick Start

Add the dependency to your Cargo.toml:

[dependencies]
exo-backend-classical = "0.1"

Basic usage:

use exo_backend_classical::ClassicalBackend;
use exo_core::SubstrateBackend;

let backend = ClassicalBackend::new()
    .with_simd(true)
    .with_dither_quantization(8); // 8-bit dithered

// Run a forward pass
let output = backend.forward(&input_tensor)?;

// Check thermodynamic cost
println!("Energy: {:.4} kT", backend.energy_cost());

// Cross-domain transfer (5-phase pipeline)
let result = backend.transfer("vision", "language", &payload)?;
println!("Transfer score: {:.4}", result.quality);

Crate Layout

Requirements

• Rust 1.78+
• Depends on exo-core
• Optional: AVX2-capable CPU for best SIMD performance

Links

• GitHub
• EXO-AI Documentation

License

MIT OR Apache-2.0