moon-tensor

MoonBit neural network inference operators for small, portable deployments.

moon-tensor aims to be the first neural network inference operator library in the MoonBit ecosystem. It focuses on simple, readable, dependency-light kernels that compile cleanly to WebAssembly, JavaScript, and native targets.

Features

• GEMM: row-major matrix multiplication
• Conv1D: no-padding, stride-1 1D convolution
• Linear: fully connected layer with bias
• Activations: ReLU, Sigmoid, GELU
• Softmax: Softmax and LogSoftmax
• Normalization: LayerNorm and RMSNorm
• Pooling: MaxPool1d and AvgPool1d
• Utilities: max absolute difference for numerical checks

Quick Start

Add moon-tensor as a dependency in your MoonBit project, then import it from the package that uses the operators.

import {
  "Ankaluoer/moon-tensor" @tensor,
}

Example:

///|
fn main {
  let input = [1.0, 2.0, 3.0]
  let activated = @tensor.relu(input)
  println(activated)

  let logits = [1.0, 2.0, 3.0]
  let probs = @tensor.softmax(logits)
  println(probs)
}

Run tests:

moon test

Build for WebAssembly GC:

moon build --target wasm-gc

Size Comparison

Runtime or library	Approximate artifact size
moon-tensor wasm-gc demo	10 KB
ORT-Web	25 MB
TensorFlow.js core-style bundles	300-500 KB

The goal is not to replace full ML runtimes. moon-tensor targets tiny inference paths where a small set of predictable operators is enough.

Roadmap

• Quantized kernels for smaller models and faster edge inference
• More inference operators, including Conv2D and attention building blocks
• Keyword spotting demo for an end-to-end small model workflow
• More backend checks across wasm-gc, wasm, js, and native targets

License

Apache-2.0