┌─────────────────────────────────────────────────────────────┐
│                    Application Layer                          │
│  视频会议业务逻辑（会议控制、布局引擎、QoS 自适应）            │
├─────────────────────────────────────────────────────────────┤
│                  WebNN Pipeline Orchestrator                  │
│  Graph Compiler │ Memory Planner │ Schedule Executor         │
├─────────────────────────────────────────────────────────────┤
│                    WebNN Runtime Abstraction                  │
│  WebNN API (MLContext, MLGraph, MLCommandEncoder)            │
├─────────────────────────────────────────────────────────────┤
│  Backend: WebGPU │ DirectML │ CoreML │ OpenVINO │ CPU (WASM) │
└─────────────────────────────────────────────────────────────┘

// 1. 从 VideoFrame 导入 GPUExternalTexture
const externalTexture = gpuDevice.importExternalTexture({
  source: videoFrame,
  colorSpace: 'srgb',
});

// 2. WebNN 图定义：YUV→RGB + Resize + Normalize 融合
const graphBuilder = new MLGraphBuilder(context);
const input = graphBuilder.input('video_input', { 
  type: 'float32', 
  dimensions: [1, 3, 720, 1280] 
});

// 算子融合：ColorSpaceConversion + ResizeBilinear + Normalize
const yuvToRgb = graphBuilder.colorSpaceConversion(input, 'bt601-limited');
const resized = graphBuilder.resizeBilinear(yuvToRgb, [288, 512]);
const normalized = graphBuilder.normalize(resized, 
  { mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225] }
);

// 3. 编译与执行：零拷贝绑定外部纹理
const graph = await graphBuilder.build({ output: normalized });
const encoder = context.createCommandEncoder();
const binding = encoder.createBinding(graph, {
  'video_input': { resource: externalTexture }  // 关键：直接绑定 GPUExternalTexture
});
encoder.dispatch(graph, binding);

sequenceDiagram
    participant AudioWorklet as AudioWorklet (48kHz)
    participant RingBuffer as Ring Buffer (512帧)
    participant WebNN as WebNN Graph
    participant Output as 输出队列
    
    AudioWorklet->>RingBuffer: push 10ms frame (480 samples)
    RingBuffer->>WebNN: 拼接上下文帧 (512 samples)
    WebNN->>WebNN: Conv1D + GRU (状态张量循环)
    WebNN->>Output: 输出增强语音 + VAD 概率
    Output->>AudioWorklet: 播放/编码

// 共享编码器
const encoderOut = graphBuilder.mobileNetV3Encoder(input, 'shared_encoder');

// 任务头 1：超分 (ESPCN)
const srHead = graphBuilder.pixelShuffle(
  graphBuilder.conv2d(encoderOut, { outChannels: 12, kernelSize: [3,3] }),
  2  // 2x 放大
);

// 任务头 2：人像分割
const segHead = graphBuilder.sigmoid(
  graphBuilder.conv2d(encoderOut, { outChannels: 1, kernelSize: [1,1] })
);

// 单图双出口编译
const multiTaskGraph = await graphBuilder.build({ 
  superResolution: srHead, 
  segmentation: segHead 
});

Time →
Frame N:     [Preprocess] → [Inference] → [Postprocess] → [Submit]
Frame N+1:            [Preprocess] → [Inference] → [Postprocess] → [Submit]
Frame N+2:                   [Preprocess] → [Inference] → [Postprocess] → [Submit]

async function createBestContext(): Promise<MLContext> {
  const backends = ['webgpu', 'webgl', 'wasm'] as const;
  
  for (const backend of backends) {
    try {
      const context = await navigator.ml.createContext({ 
        deviceType: 'gpu', 
        powerPreference: 'high-performance',
        backendHint: backend  // 非标准扩展，Chrome/Edge 支持
      });
      // 预热校验
      await warmup(context);
      console.log(`[WebNN] Backend selected: ${backend}`);
      return context;
    } catch (e) {
      console.warn(`[WebNN] Backend ${backend} unavailable:`, e);
    }
  }
  throw new Error('No WebNN backend available');
}

interface WebNNTelemetry {
  // 延迟分解 (ms)
  latency: {
    preprocess: number;
    inference: number;
    postprocess: number;
    gpuSubmit: number;
    total: number;
  };
  // 资源占用
  memory: {
    peakHeapMB: number;
    peakVRAMMB: number;
    bufferCount: number;
  };
  // 质量指标
  quality: {
    fps: number;
    frameDropRate: number;
    mosScore: number;      // 音频主观质量估算
    niqeScore: number;     // 视频无参考质量评估
  };
  // 硬件环境
  env: {
    backend: string;
    adapterInfo: GPUAdapterInfo;
    thermalState: 'nominal' | 'fair' | 'serious' | 'critical';
  };
}

WebNN MLGraph (Public API)
        │
        ▼
┌───────────────────┐
│  Graph IR (MIL)   │  ← 算子融合、常量折叠、Layout 统一、动态 Shape 符号化推导
│  (Model IR Layer) │
└─────────┬─────────┘
          │ Lowering (Pattern Match & Rewrite)
          ▼
┌───────────────────┐
│  Hardware IR (HIL)│  ← 硬件感知：Tile 划分、双缓冲 Async Copy、Subgroup Matrix Mul (WMMA)
│ (Hardware IR Layer)│
└─────────┬─────────┘
          │ CodeGen (Template + Schedule)
          ▼
    WGSL Shader Module + Binding Layout Descriptor

// 伪代码：融合收益评估器
struct FusionProfit {
  float arithmetic_intensity; // FLOPs/Byte
  size_t register_pressure;   // 估算 VGPR/SGPR 占用
  size_t shared_mem_usage;    // Workgroup Memory 需求
  bool is_profitable(const HardwareSpec& hw) const {
    // 1. 计算强度 > 硬件 Ridge Point (如 Adreno 740 ~ 128 FLOPs/Byte) 才考虑融合
    // 2. 寄存器压力 < 限制 (如 255 VGPR) 避免 Occupancy 崩塌
    // 3. Shared Memory < 16KB (留 48KB 给 Occupancy)
    return arithmetic_intensity > hw.ridge_point 
        && register_pressure < hw.max_vgpr * 0.8
        && shared_mem_usage < hw.shared_mem_per_sm * 0.75;
  }
};

// 融合 Pass 伪逻辑
void GreedyFusionPass(Graph& g) {
  for (auto node : g.nodes_in_topo_order()) {
    if (node->is_elementwise() || node->is_broadcast()) {
      auto producer = node->get_single_producer();
      if (producer && FusionProfit::estimate(producer, node).is_profitable(target_hw)) {
        fuse_into_consumer(producer, node); // 将 producer 逻辑内联到 node 的 Shader 中
      }
    }
  }
}

// 编译期：生成参数化 Shader 模板
const symbolicGraph = compiler.compileSymbolic(graph, {
  'input': ['N', 'C', 'H', 'W'],  // 符号维度
  'weight': ['O', 'I', 'kH', 'kW']
});

// 产出：WGSL 模板字符串 + 符号约束条件
// 例如：workgroup_count = ceil(H/8) * ceil(W/8) * ceil(O/4)

// 运行期：实例化缓存键设计
interface CacheKey {
  shaderTemplateHash: string;      // 模板结构哈希
  concreteShapes: Map<string, number[]>; // 具体 Shape: {input: [1,3,720,1280]}
  pipelineLayoutHash: string;      // Bind Group Layout 哈希
}

// LRU 缓存策略：最多缓存 32 组 Shape 实例，命中率 > 95%
const shaderCache = new LRUCache<CacheKey, GPUComputePipeline>(32);

// 简化版：INT4 Packed Weight (2 elements per byte) + FP16 Activation
// 利用 Subgroup (Wavefront/Warps) 协作完成 16x16x16 MMA
@compute @workgroup_size(64) // 1 Wavefront (AMD) / 2 Warps (NVIDIA)
fn main(@builtin(global_invocation_id) gid: vec3<u32>) {
  // 1. 从 Storage Buffer 加载 INT4 权重 (Packed U8) 到 Workgroup Memory
  // 2. 解包为 FP16 (或直接用 DP4A 指令模拟，WebGPU 暂无原生 INT4 MMA)
  // 3. 使用 subgroupMatrixMultiplyAccumulate (f16) 计算
  
  // 关键优化：Double Buffering + Async Copy (workgroupBarrier)
  var<workgroup> As: array<f16, 16*16>; // Tile A
  var<workgroup> Bs: array<f16, 16*16>; // Tile B (Dequantized on the fly)
  
  // ... 省略加载与解包逻辑 ...
  
  // Subgroup Level MMA (16x16x16)
  let fragA = subgroupLoadMatrix(As, ...);
  let fragB = subgroupLoadMatrix(Bs, ...);
  var fragC = subgroupMatrixMultiplyAccumulate(fragA, fragB, zero_matrix);
  
  // 写回 Global Memory
  subgroupStoreMatrix(output, fragC, ...);
}

class KVCacheManager {
  private kCache: GPUBuffer; // [MaxSeqLen, NumHeads, HeadDim]
  private vCache: GPUBuffer;
  private writeHead: number = 0;
  private readonly windowSize: number = 2048; // 滑动窗口
  
  // 编码器写入新 KV
  append(newK: GPUBuffer, newV: GPUBuffer, seqLen: number): void {
    const encoder = device.createCommandEncoder();
    // 处理环形覆盖：若 writeHead + seqLen > MaxSeqLen，分两次 copy
    this.copyWithWrap(encoder, this.kCache, newK, this.writeHead, seqLen);
    this.copyWithWrap(encoder, this.vCache, newV, this.writeHead, seqLen);
    this.writeHead = (this.writeHead + seqLen) % this.maxSeqLen;
    device.queue.submit([encoder.finish()]);
  }

  // Attention Kernel 读取：构建有效序列索引映射
  getAttentionMaskAndIndices(currentSeqLen: number): { mask: GPUBuffer, indices: GPUBuffer } {
    // 生成因果掩码 + 滑动窗口掩码 (1 表示可见)
    // 索引缓冲区用于 Kernel 中 gather 有效 K/V，避免无效计算
  }
}

Timeline (Unix Timestamp ms)
│
├─ Video Track: [Frame@1000] [Frame@1033] [Frame@1066] ... (30fps)
├─ Audio Track: [Chunk@1000-1020] [Chunk@1020-1040] ... (20ms/frame)
└─ Text Track:  [User Msg@1050] [Doc Update@1100] ...

sequenceDiagram
    participant User
    participant Orchestrator as 编排器
    participant LLM as Phi-3 Decoder
    participant TTS as 浏览器 TTS / WebAudio
    
    User->>Orchestrator: 打断信号 (按键/唤醒词)
    Orchestrator->>LLM: 发送 AbortSignal + 当前生成的 Partial Tokens
    LLM->>LLM: 停止采样，保留 KV Cache 至打断点
    Orchestrator->>TTS: 停止播放, 清空音频队列
    User->>Orchestrator: 新指令 (语音/文本)
    Orchestrator->>LLM: 拼接 [System Prompt, History, Partial_Response, New_Instruction]
    LLM->>Orchestrator: 流式生成新回复 (复用 KV Cache)

部署包结构：
/models
  ├─ phi3_int4_encrypted.bin   (AES-256-GCM 加密)
  ├─ whisper_encoder_encrypted.bin
  └─ manifest.json             (含 IV, Salt, 版本号)

/wasm
  └─ model_decryptor.wasm      (Rust 编译，含解密逻辑 + 密钥派生)

// 客户端本地训练循环 (WebWorker + WebNN)
async function localTrainingRound(globalModel: MLGraph, localData: Dataset) {
  // 1. 克隆全局模型权重到本地 Buffer
  const localWeights = await cloneWeights(globalModel);
  
  // 2. 本地微调 (LoRA 适配器仅 0.5% 参数量)
  const optimizer = new SGD({ lr: 1e-4 });
  for (const batch of localData.batches(8)) {
    const loss = await forwardBackward(localWeights, batch); // WebNN 训练模式
    optimizer.step(localWeights.loraParams, loss.gradients);
  }
  
  // 3. 计算模型更新量 (Delta)
  const delta = computeDelta(globalWeights, localWeights);
  
  // 4. 安全聚合准备：本地加噪 (DP-SGD) + 加密
  const noisyDelta = addGaussianNoise(delta, sigma=0.01);
  const encryptedDelta = await encryptForServer(noisyDelta); // Threshold Paillier / CKKS
  
  return encryptedDelta; // 仅上传加密更新，原始数据、梯度不出浏览器
}

# .github/workflows/webnn-conformance.yml
jobs:
  numerical_correctness:
    strategy:
      matrix:
        os: [ubuntu-latest, windows-latest, macos-latest]
        browser: [chrome-stable, chrome-beta, edge-stable, firefox-stable, safari-tp]
        model: [whisper-tiny, phi3-mini-int4, yolov8n-seg, rnnoise]
    runs-on: ${{ matrix.os }}
    steps:
      - uses: actions/checkout@v4
      - name: Setup Browser & WebDriver
        uses: browser-actions/setup-browser@v1
        with: { browser: ${{ matrix.browser }} }
      - name: Run Numerical Diff
        run: |
          python -m pytest tests/numerical/ 
            --model=${{ matrix.model }} 
            --browser=${{ matrix.browser }} 
            --tolerance=strict 
            --output=artifacts/${{ matrix.os }}-${{ matrix.browser }}-${{ matrix.model }}.json
      - name: Upload Diff Report
        uses: actions/upload-artifact@v4
        with: { name: diff-report, path: artifacts/ }

  performance_regression:
    # 夜ly 跑 50+ 真机设备矩阵 (Device Farm)
    # 关注 P50/P95/P99 Latency, Memory, Power