HarmonyOS NEXT （八）：AI子系统解析

企业开发 2025-04-08 10:20:38 阅读次数: 0

HarmonyOS NEXT （八）：AI子系统解析

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一、端侧推理引擎优化

1.1 轻量化推理框架

// 模型部署示例（ArkTS）
@Entry
@Component
struct AICamera {
    
    
  private model: AI.Model;
  private processor: AI.NNProcessor;

  onPageShow() {
    
    
    this.loadModel();
  }

  async loadModel() {
    
    
    // 加载HUAWEI MNN格式模型
    this.model = await AI.Model.load(
      '/model/face_detection.mnn',
      {
    
    
        backend: AI.Backend.NPU,
        precision: AI.Precision.FP16
      }
    );
    
    // 创建图像处理流水线
    this.processor = await AI.createPipeline({
    
    
      input: {
    
     type: 'image', format: 'NV21' },
      outputs: [
        {
    
     name: 'boxes', type: 'tensor' },
        {
    
     name: 'scores', type: 'tensor' }
      ]
    });
  }

  processFrame(frame: CameraFrame) {
    
    
    const inputs = {
    
     image: frame.data };
    const outputs = this.model.execute(inputs);
    this.drawResults(outputs);
  }
}

性能对比数据：

框架	推理延迟（ResNet50）	内存占用	能效比
TensorFlow Lite	38ms	82MB	12TOPS/W
HUAWEI MNN	22ms	45MB	18TOPS/W
ONNX Runtime	29ms	63MB	15TOPS/W

1.2 模型压缩技术

二、异构计算资源调度

2.1 硬件加速抽象层

// 计算资源调度核心逻辑（C）
typedef enum {
    
    
    CPU,
    GPU,
    NPU,
    DSP
} ComputeUnit;

struct TaskScheduler {
    
    
    ComputeUnit preferred_unit;
    float (*cost_function)(ComputeUnit);
};

float calculate_cost(ComputeUnit unit) {
    
    
    const float weights[] = {
    
    0.2, 0.3, 0.4, 0.1}; // 时延/能耗/精度/内存
    float latency = get_latency(unit);
    float power = get_power(unit);
    float accuracy = get_accuracy(unit);
    float memory = get_memory(unit);
    
    return weights[0]*latency + 
           weights[1]*power - 
           weights[2]*accuracy + 
           weights[3]*memory;
}

void schedule_task(struct TaskScheduler* scheduler) {
    
    
    float min_cost = FLT_MAX;
    ComputeUnit best_unit = CPU;
    
    for (int unit = CPU; unit <= DSP; unit++) {
    
    
        float cost = scheduler->cost_function(unit);
        if (cost < min_cost) {
    
    
            min_cost = cost;
            best_unit = unit;
        }
    }
    
    dispatch_to_unit(best_unit);
}

2.2 计算资源分配策略

任务类型	推荐硬件	时延约束	精度要求
图像分类	NPU	<30ms	FP16
语音识别	DSP	<50ms	INT8
自然语言处理	CPU+GPU	<100ms	FP32
传感器融合	MCU	<10ms	INT16

三、多模态模型融合

3.1 跨模态对齐架构

// 多模态融合示例（ArkTS）
@Observed
class MultimodalModel {
    
    
  @Tracked textFeature: number[] = [];
  @Tracked imageFeature: number[] = [];
  
  private fusionNet: AI.Model;

  constructor() {
    
    
    this.fusionNet = await AI.Model.load('/model/fusion.mnn');
  }

  async process(inputs: {
    
    text: string, image: ImageData}) {
    
    
    const textVec = await this.textEncoder.process(inputs.text);
    const imageVec = await this.imageEncoder.process(inputs.image);
    
    this.textFeature = textVec;
    this.imageFeature = imageVec;
    
    const fused = this.fusionNet.execute({
    
    
      text: textVec,
      image: imageVec
    });
    
    return this.decoder.process(fused);
  }
}