Compiling C++ Neural Networks to WebAssembly: Matrix Math & SIMD in the Browser

Building machine learning models from scratch in low-level languages is one of the most effective ways to understand how backpropagation, matrix multiplication, and memory layout affect computational throughput.

In nn-number-rec, I built a multilayer perceptron in C++ and compiled it to WebAssembly (WASM) using Emscripten to achieve near-native digit classification in the browser.

1. Cache-Friendly Matrix Operations

In standard JavaScript, array allocations can introduce GC pauses and irregular memory strides. In C++, we control memory layout precisely:

cpp

// Contiguous flat buffer for weights to maximize L1/L2 cache hits
std::vector<float> weights;

2. Emscripten Compilation & Vectorization

By targeting the WebAssembly SIMD specification (-msimd128), 128-bit vector registers process multiple float multiplications in parallel per CPU cycle:

bash

em++ -std=c++11 ./cpp/wasm.cpp -O3 -flto -msimd128 -s WASM=1 -o ./js/logic.js

3. Real-Time Canvas Interactivity

The compiled WASM module exposes a fast C function to JavaScript that accepts pixel buffers directly from an HTML5 Canvas drawing element, classifying the digit in under 1 millisecond.