ruvector-dither

Deterministic, low-discrepancy pre-quantization dithering for low-bit neural network inference on tiny devices (WASM, Seed, STM32).

Why dither?

Quantizers at 3/5/7 bits can align with power-of-two boundaries, producing idle tones, sticky activations, and periodic errors that degrade accuracy. A sub-LSB pre-quantization offset:

• Decorrelates the signal from grid boundaries.
• Pushes quantization error toward high frequencies (blue-noise-like), which average out downstream.
• Uses no RNG -- outputs are deterministic, reproducible across platforms (WASM / x86 / ARM), and cache-friendly.

Features

• Golden-ratio sequence -- best 1-D equidistribution, irrational period (never repeats).
• Pi-digit table -- 256-byte cyclic lookup, exact reproducibility from a tensor/layer ID.
• Per-channel dither pools -- structurally decorrelated channels without any randomness.
• Scalar, slice, and integer-code quantization helpers included.
• no_std-compatible -- zero runtime dependencies; enable with features = ["no_std"].

Quick start

use ruvector_dither::{GoldenRatioDither, PiDither, quantize_dithered};

// Golden-ratio dither, 8-bit, epsilon = 0.5 LSB
let mut gr = GoldenRatioDither::new(0.0);
let q = quantize_dithered(0.314, 8, 0.5, &mut gr);
assert!(q >= -1.0 && q <= 1.0);

// Pi-digit dither, 5-bit
let mut pi = PiDither::new(0);
let q2 = quantize_dithered(0.271, 5, 0.5, &mut pi);
assert!(q2 >= -1.0 && q2 <= 1.0);

Per-channel batch quantization

use ruvector_dither::ChannelDither;

let mut cd = ChannelDither::new(/*layer_id=*/ 0, /*channels=*/ 8, /*bits=*/ 5, /*eps=*/ 0.5);
let mut activations = vec![0.5_f32; 64]; // shape [batch=8, channels=8]
cd.quantize_batch(&mut activations);

Modules

Trait: DitherSource

Implement DitherSource to plug in your own deterministic sequence:

pub trait DitherSource {
    /// Return the next zero-mean offset in [-0.5, +0.5].
    fn next_unit(&mut self) -> f32;
}

License

Licensed under either of Apache License, Version 2.0 or MIT License at your option.