#码力全开·技术π对#在 Chrome 中集成 AI API，如何通过 WebAssembly 提升本地计算效率？

// Rust + WASM 模型量化示例
#[wasm_bindgen]
pub fn quantize_model(fp32_weights: &[f32]) -> Vec<u8> {
    let mut quantized = Vec::with_capacity(fp32_weights.len());
    for &val in fp32_weights {
        quantized.push((val * 255.0).clamp(0.0, 255.0) as u8);
    }
    quantized
}

// C++ Emscripten SIMD 示例
#include <wasm_simd128.h>

v128_t inference_step(v128_t* weights, v128_t* inputs){
    v128_t sum = wasm_f32x4_splat(0.0f);
    for(int i=0; i<128; i+=4) {
        sum = wasm_f32x4_add(sum, 
            wasm_f32x4_mul(weights[i], inputs[i]));
    }
    return sum;
}

// JavaScript 端内存管理
const memory = new WebAssembly.Memory({ initial: 256 }); // 256页 = 16MB
const wasmInstance = await WebAssembly.instantiate(module, {
    env: { memory }
});

// 将 WASM 计算与 UI 渲染分离
const offscreen = new OffscreenCanvas(300, 300);
const ctx = offscreen.getContext('2d');
requestAnimationFrame(() => {
    wasmModule.processFrame(offscreen);
});

// 专用 Worker 保持 WASM 上下文
const aiWorker = new Worker('ai-worker.js');
aiWorker.postMessage({type: 'init', wasmBinary});

// worker.js
WebAssembly.instantiate(fetch('ai.wasm')).then(...);

// 分片加载模型权重
#[wasm_bindgen]
pub fn load_model_chunk(offset: usize, chunk: &[u8]) {
    unsafe {
        let ptr = WASM_MEMORY_PTR.add(offset);
        std::ptr::copy_nonoverlapping(chunk.as_ptr(), ptr, chunk.len());
    }
}

if (!WebAssembly.validate(binary)) {
    fallbackToTensorFlowJS(); 
}

// 加载并初始化 WASM 模块
fetch('ai_model.wasm').then(response => 
  response.arrayBuffer()
).then(bytes => 
  WebAssembly.instantiate(bytes)
).then(results => {
  const { classifyImage, getOutput } = results.instance.exports;

  // 假设 imageBuffer 是预处理后的图像数据
  const resultPtr = classifyImage(imageBuffer);
  const output = new Float32Array(wasmMemory.buffer, resultPtr, 10); // 读取输出
  console.log("Classification result:", output);
});