第26章：软硬件协同优化

前端(Frontend)     : 模型解析，格式转换
    ↓
高层IR(High-level IR): 算子融合，图优化
    ↓  
中层IR(Mid-level IR) : 张量化，循环优化
    ↓
低层IR(Low-level IR) : 指令选择，寄存器分配
    ↓
目标代码(Target Code) : 汇编/二进制

# 伪代码：编译时量化策略选择
for layer in model.layers:
    if layer.is_compute_bound():
        # 计算密集层：激进量化
        layer.weight_bits = 4
        layer.activation_bits = 8
    else:
        # 内存密集层：保守量化
        layer.weight_bits = 8
        layer.activation_bits = 8

// 优化前：频繁切换功能单元
LOAD  r1, [addr1]  // 内存单元
ADD   r2, r1, r3  // ALU单元
LOAD  r4, [addr2]  // 内存单元
MUL   r5, r2, r4  // 乘法单元

// 优化后：批量执行同类操作
LOAD  r1, [addr1]  // 内存单元
LOAD  r4, [addr2]  // 内存单元
ADD   r2, r1, r3  // ALU单元
MUL   r5, r2, r4  // 乘法单元

硬件设计 ←→ 编译器设计
   ↓           ↓
性能模型   功耗模型
   ↓           ↓
   统一优化目标

未融合：Conv → ReLU → BatchNorm
内存访问：3次写入 + 2次读取

融合后：Conv-ReLU-BN
内存访问：1次写入 + 0次读取（中间结果保持在寄存器）

// 垂直融合示例
原始图：
Input → Conv1 → ReLU1 → Conv2 → ReLU2

融合后：
Input → [Conv1+ReLU1] → [Conv2+ReLU2]

// 水平融合示例
原始图：
Input → Split → [Conv1] → Concat
              → [Conv2] →

融合后：
Input → [Conv1+Conv2并行] → Concat

# 规则1：连续转置消除
Transpose(Transpose(x, perm1), perm2) → Transpose(x, compose(perm1, perm2))

# 规则2：恒等变换消除
Reshape(Reshape(x, shape1), shape2) → Reshape(x, shape2)

# 规则3：常量折叠
Add(Const(a), Const(b)) → Const(a+b)

# NCHW vs NHWC布局选择
if hardware.prefer_channel_last:
    insert_layout_transform(graph, "NHWC")
else:
    insert_layout_transform(graph, "NCHW")

# 将除法转换为乘法
Div(x, Const(c)) → Mul(x, Const(1/c))

# 将乘法转换为移位（2的幂次）
Mul(x, Const(2^n)) → Shift(x, n)

# 动态跳过零稀疏区域
if input.sparsity > threshold:
    execute_sparse_kernel()
else:
    execute_dense_kernel()

# 根据输入尺寸选择最优实现
if input_size < 32:
    use_direct_convolution()
elif input_size < 256:
    use_winograd_convolution()
else:
    use_fft_convolution()

性能 ↑
   │  ○ 配置A（高性能）
   │ ○ 
   │○   配置B（平衡）
   │  ○
   │    ○ 配置C（低功耗）
   └──────────→ 功耗

# 多约束优化问题
minimize: energy_consumption
subject to:
    latency <= target_latency
    memory_usage <= available_memory
    accuracy_loss <= tolerance

传统分配：每个张量独立分配
┌──┐┌──┐┌──┐┌──┐
│T1││T2││T3││T4│  总内存：4个单位
└──┘└──┘└──┘└──┘

池化分配：复用内存空间
┌────────┐
│T1→T3→T4│  总内存：2个单位
├────────┤
│   T2   │
└────────┘

# 张量生命周期重叠检测
def can_share_memory(tensor1, tensor2):
    return not overlaps(tensor1.lifetime, tensor2.lifetime)

# 构建冲突图
conflict_graph = build_conflict_graph(tensors)
# 图着色算法分配内存
memory_assignment = graph_coloring(conflict_graph)

// 缓存行对齐减少false sharing
#define CACHE_LINE_SIZE 64
struct aligned_tensor {
    float data[SIZE];
} __attribute__((aligned(CACHE_LINE_SIZE)));

决策树：
if (访问频率 > 阈值1) {
    放置在L1缓存
} else if (访问频率 > 阈值2) {
    放置在L2缓存
} else if (重用距离 < 阈值3) {
    放置在L3缓存
} else {
    放置在DRAM
}

// 软件预取降低访存延迟
for (i = 0; i < N; i++) {
    __builtin_prefetch(&data[i+PREFETCH_DISTANCE], 0, 1);
    process(data[i]);
}

# 缓存容量分配
total_cache = 2MB
weight_cache = total_cache * 0.3  # 30%给权重
activation_cache = total_cache * 0.5  # 50%给激活值
workspace_cache = total_cache * 0.2  # 20%给临时空间

# 动态压缩策略
if memory_pressure > threshold:
    # 压缩不常用张量
    compress_cold_tensors()
    # 量化中间结果
    quantize_intermediates()

# LRU替换策略
class MemoryManager:
    def allocate(self, size):
        if not enough_space(size):
            # 换出最少使用的张量
            evict_lru_tensors(size)
        return allocate_space(size)

碎片化内存：
[已用][空闲][已用][空闲][已用]

整理后：
[已用][已用][已用][空闲空闲]

// 原始：大步长访问
for (i = 0; i < M; i++)
    for (j = 0; j < N; j++)
        C[i][j] = A[i][j] + B[i][j];

// Tiling后：提高局部性
for (ii = 0; ii < M; ii += TILE)
    for (jj = 0; jj < N; jj += TILE)
        for (i = ii; i < min(ii+TILE, M); i++)
            for (j = jj; j < min(jj+TILE, N); j++)
                C[i][j] = A[i][j] + B[i][j];

# 将离散数据打包成连续块
def pack_weights(weights):
    # NCHW → NC/4HW4 (4通道打包)
    packed = weights.reshape(N, C//4, 4, H, W)
    return packed.transpose(0, 1, 3, 4, 2)

# 使用内存映射避免拷贝
tensor_view = create_view(original_tensor, offset, shape)
# 直接在原始内存上操作
process_in_place(tensor_view)

# 激活值重计算（梯度检查点技术）
def forward_with_recompute(x):
    # 不保存中间激活值
    y1 = layer1(x)  # 计算但不存储
    y2 = layer2(y1)  # 
    # 反向传播时重新计算
    return y2

// 利用DRAM突发传输特性
#define BURST_SIZE 64  // 字节
void optimized_memcpy(void* dst, void* src, size_t size) {
    // 对齐到突发边界
    size_t aligned_size = ALIGN(size, BURST_SIZE);
    burst_transfer(dst, src, aligned_size);
}

功耗效率曲线：
效率↑
   │     ╱─────── 饱和区
   │   ╱ 
   │ ╱   最优点
   │╱
   └────────────→ 批大小
   1  4  8  16  32

class AdaptiveBatcher:
    def __init__(self, max_latency, max_batch):
        self.max_latency = max_latency
        self.max_batch = max_batch

    def get_batch_size(self, queue_length, current_latency):
        if current_latency > self.max_latency * 0.8:
            return 1  # 降低批大小保证延迟
        elif queue_length > self.max_batch:
            return self.max_batch  # 最大吞吐
        else:
            # 动态调整
            return min(queue_length, 
                      self.estimate_optimal_batch())

# 不同精度请求的批处理
def heterogeneous_batching(requests):
    int8_batch = filter(lambda r: r.precision == 'int8', requests)
    fp16_batch = filter(lambda r: r.precision == 'fp16', requests)

    # 分别处理不同精度批次
    process_int8_batch(int8_batch)  # 低功耗路径
    process_fp16_batch(fp16_batch)  # 高精度路径

# 预测性执行降低感知延迟
def speculative_inference(input):
    # 启动快速低精度推理
    fast_result = quick_inference(input)

    # 并行启动精确推理
    precise_future = async_precise_inference(input)

    # 如果置信度足够，直接返回
    if fast_result.confidence > threshold:
        cancel(precise_future)  # 取消精确推理，节省功耗
        return fast_result
    else:
        return wait(precise_future)

# 动态深度网络
class EarlyExitNetwork:
    def forward(self, x):
        for i, layer in enumerate(self.layers):
            x = layer(x)
            if i in self.exit_points:
                confidence = self.exit_classifiers[i](x)
                if confidence > self.thresholds[i]:
                    return self.final_classifiers[i](x)
        return self.final_output(x)

时间轴：
T0: [层1:批1] [空闲    ] [空闲    ]
T1: [层2:批1] [层1:批2] [空闲    ]
T2: [层3:批1] [层2:批2] [层1:批3]
T3: [输出:批1][层3:批2] [层2:批3]

class PowerAwareScheduler:
    def schedule(self, requests):
        # 根据功耗代价排序
        sorted_requests = sorted(requests, 
                               key=lambda r: self.power_cost(r))

        # 功耗预算约束下调度
        scheduled = []
        current_power = 0
        for req in sorted_requests:
            if current_power + req.power <= self.power_budget:
                scheduled.append(req)
                current_power += req.power
            else:
                # 等待下一时间片
                self.defer(req)
        return scheduled

# 相似请求合并处理
def merge_similar_requests(requests):
    clusters = {}
    for req in requests:
        key = (req.model, req.precision, req.batch_dim)
        if key not in clusters:
            clusters[key] = []
        clusters[key].append(req)

    # 批量处理每个簇
    for key, reqs in clusters.items():
        batch_process(reqs)  # 共享计算降低功耗

# 多加速器负载分配
def power_aware_load_balance(requests, accelerators):
    for req in requests:
        # 选择能效最优的加速器
        best_acc = None
        best_efficiency = 0

        for acc in accelerators:
            if acc.can_handle(req):
                efficiency = acc.ops_per_watt(req)
                if efficiency > best_efficiency:
                    best_efficiency = efficiency
                    best_acc = acc

        best_acc.enqueue(req)

class ThermalAwareScheduler:
    def adjust_frequency(self, temperature):
        if temperature > CRITICAL_TEMP:
            # 紧急降频
            return self.min_frequency
        elif temperature > WARNING_TEMP:
            # 渐进降频
            return self.current_freq * 0.9
        else:
            # 正常运行
            return self.target_freq

    def migrate_workload(self, hot_cores, cool_cores):
        # 将负载从热核迁移到冷核
        for task in hot_cores.get_tasks():
            if cool_cores.has_capacity():
                cool_cores.enqueue(task)

# 移动设备电池优化
def battery_aware_inference(model, input, battery_level):
    if battery_level < 20:
        # 低电量：最低功耗模式
        return model.forward_int4(input)
    elif battery_level < 50:
        # 中等电量：平衡模式
        return model.forward_int8(input)
    else:
        # 充足电量：最佳质量
        return model.forward_fp16(input)

# 基于历史的负载预测
class LoadPredictor:
    def __init__(self, window_size=100):
        self.history = deque(maxlen=window_size)

    def predict_next_load(self):
        if len(self.history) < 10:
            return self.default_load

        # 时间序列预测
        return self.arima_model.predict(self.history)

    def preactivate_resources(self, predicted_load):
        if predicted_load > self.threshold:
            # 提前唤醒休眠单元
            wake_up_accelerators()

def pareto_optimal_schedule(requests, constraints):
    solutions = []

    for config in generate_configs():
        latency = estimate_latency(config)
        power = estimate_power(config)

        # 检查约束
        if latency <= constraints.max_latency and \
           power <= constraints.max_power:
            solutions.append((config, latency, power))

    # 返回帕累托前沿
    return get_pareto_front(solutions)

class SLAManager:
    def __init__(self, latency_sla, power_budget):
        self.latency_sla = latency_sla
        self.power_budget = power_budget

    def adjust_operating_point(self, current_metrics):
        if current_metrics.latency > self.latency_sla:
            # 违反延迟SLA，提高性能
            increase_frequency()
            reduce_batch_size()
        elif current_metrics.power > self.power_budget:
            # 超出功耗预算，降低功耗
            decrease_frequency()
            enable_power_gating()
        else:
            # 在约束内优化
            optimize_efficiency()

# 使用RL学习最优调度策略
class RLScheduler:
    def __init__(self):
        self.q_table = {}  # 状态-动作值函数

    def select_action(self, state):
        # ε-贪婪策略
        if random.random() < self.epsilon:
            return random.choice(self.actions)
        else:
            return argmax(self.q_table[state])

    def update(self, state, action, reward, next_state):
        # Q-learning更新
        old_q = self.q_table[state][action]
        next_max = max(self.q_table[next_state].values())
        new_q = old_q + self.alpha * (reward + self.gamma * next_max - old_q)
        self.q_table[state][action] = new_q

    def compute_reward(self, latency, power):
        # 奖励函数：平衡延迟和功耗
        return -(self.w1 * latency + self.w2 * power)

TensorRT融合模式：
• Convolution + Bias + ReLU → CBR融合核
• Convolution + BatchNorm + ReLU → 单个CUDNN核
• ElementWise + Activation → 融合核
• Concat + ReLU → 内存优化融合

# TensorRT INT8校准流程
class INT8Calibrator:
    def __init__(self, data_loader):
        self.data_loader = data_loader
        self.histogram = defaultdict(list)

    def collect_statistics(self, layer, activations):
        # 收集激活值分布
        min_val, max_val = activations.min(), activations.max()
        self.histogram[layer].append((min_val, max_val))

    def compute_scale(self, layer):
        # 基于KL散度选择量化阈值
        ranges = self.histogram[layer]
        optimal_range = minimize_kl_divergence(ranges)
        return 127.0 / optimal_range

# 动态内存分配策略
class DynamicMemoryAllocator:
    def __init__(self, workspace_size):
        self.pools = {
            'persistent': MemoryPool(workspace_size * 0.3),
            'activation': MemoryPool(workspace_size * 0.5),
            'scratch': MemoryPool(workspace_size * 0.2)
        }

    def allocate(self, size, lifetime):
        if lifetime == 'weight':
            return self.pools['persistent'].alloc(size)
        elif lifetime == 'activation':
            return self.pools['activation'].alloc(size)
        else:
            return self.pools['scratch'].alloc(size)

// CUDA多流执行降低延迟
cudaStream_t streams[NUM_STREAMS];
for (int i = 0; i < NUM_STREAMS; i++) {
    cudaStreamCreate(&streams[i]);
}

// 并发执行不同层
for (int i = 0; i < num_layers; i++) {
    int stream_id = i % NUM_STREAMS;
    execute_layer<<<grid, block, 0, streams[stream_id]>>>(
        layers[i], inputs[i], outputs[i]
    );
}

# 层级精度选择
def select_precision(layer, performance_model):
    fp16_time = performance_model.predict_fp16(layer)
    fp16_power = power_model.predict_fp16(layer)

    int8_time = performance_model.predict_int8(layer)
    int8_power = power_model.predict_int8(layer)

    # 精度损失评估
    accuracy_loss = evaluate_accuracy_loss(layer, 'int8')

    if accuracy_loss < threshold and int8_power < fp16_power * 0.6:
        return 'INT8'
    elif layer.is_compute_bound():
        return 'FP16'  # Tensor Core加速
    else:
        return 'FP32'  # 内存带宽受限

// CoreML模型优化pipeline
class NeuralEngineOptimizer {
    func optimize(model: MLModel) -> MLModel {
        // 1. 算子分解：将不支持的算子分解
        let decomposed = decomposeUnsupportedOps(model)

        // 2. 图分割：CPU/GPU/ANE混合执行
        let partitions = partitionGraph(decomposed)

        // 3. 量化：针对ANE的INT8/INT16量化
        let quantized = quantizeForANE(partitions.ane)

        // 4. 内存优化：最小化设备间传输
        return optimizeMemoryTransfers(partitions, quantized)
    }
}

# 设备选择策略
def partition_graph(graph, devices=['cpu', 'gpu', 'ane']):
    partitions = []

    for subgraph in graph.get_subgraphs():
        costs = {}
        for device in devices:
            # 评估在每个设备上的代价
            compute_cost = estimate_compute_cost(subgraph, device)
            transfer_cost = estimate_transfer_cost(subgraph, device)
            power_cost = estimate_power_cost(subgraph, device)

            costs[device] = (compute_cost, transfer_cost, power_cost)

        # 选择最优设备
        best_device = select_optimal_device(costs, constraints)
        partitions.append((subgraph, best_device))

    return partitions

// Metal Performance Shaders集成
@implementation MPSOptimizedConvolution

- (void)encodeToCommandBuffer:(id<MTLCommandBuffer>)commandBuffer {
    // 创建MPS卷积核
    MPSCNNConvolution *conv = [[MPSCNNConvolution alloc]
        initWithDevice:device
        weights:weights];

    // 设置优化参数
    conv.edgeMode = MPSImageEdgeModeClamp;
    conv.destinationFeatureChannelOffset = 0;

    // 执行优化的卷积
    [conv encodeToCommandBuffer:commandBuffer
                    sourceImage:sourceImage
               destinationImage:destImage];
}
@end

// 权重压缩存储
class WeightCompressor {
    func compressWeights(_ weights: [Float]) -> CompressedWeights {
        // 1. 聚类量化
        let clusters = kMeansClustering(weights, k: 256)
        let indices = mapToClusters(weights, clusters)

        // 2. 熵编码
        let encoded = huffmanEncode(indices)

        // 3. 稀疏存储
        let sparse = createSparseRepresentation(encoded)

        return CompressedWeights(
            centroids: clusters,
            indices: sparse,
            compressionRatio: calculateRatio()
        )
    }
}

# 差分模型更新减少功耗
class IncrementalModelUpdater:
    def __init__(self, base_model):
        self.base_model = base_model
        self.delta_cache = {}

    def update(self, new_weights):
        deltas = {}
        for name, weight in new_weights.items():
            if name in self.base_model:
                # 计算权重差异
                delta = weight - self.base_model[name]

                # 只更新显著变化的权重
                if delta.abs().max() > threshold:
                    deltas[name] = compress_delta(delta)

        # 增量更新
        self.apply_deltas(deltas)
        return deltas

TensorRT (NVIDIA GPU)        CoreML (Apple Silicon)
├─ 高带宽HBM内存            ├─ 统一内存架构
├─ Tensor Core加速          ├─ Neural Engine专用单元
├─ CUDA并行执行             ├─ Metal计算管线
└─ 数据中心/边缘服务器       └─ 移动/嵌入式设备

# TensorRT：后训练量化为主
def tensorrt_quantization(model):
    calibrator = create_calibrator(calibration_data)
    quantized = quantize_model(model, calibrator)
    return optimize_for_tensorcore(quantized)

# CoreML：量化感知训练
def coreml_quantization(model):
    qat_model = prepare_qat(model)
    trained = train_with_quantization(qat_model)
    return optimize_for_ane(trained)

# 统一优化框架
class UnifiedOptimizer:
    def __init__(self, target_platform):
        self.platform = target_platform
        self.optimizers = {
            'tensorrt': TensorRTOptimizer(),
            'coreml': CoreMLOptimizer(),
            'tflite': TFLiteOptimizer()
        }

    def optimize(self, model):
        # 通用优化
        model = common_optimizations(model)

        # 平台特定优化
        optimizer = self.optimizers[self.platform]
        return optimizer.optimize(model)

# ML驱动的编译优化
class LearnedOptimizer:
    def __init__(self):
        self.optimization_model = load_pretrained_model()

    def optimize(self, graph):
        # 提取图特征
        features = extract_graph_features(graph)

        # 预测最优优化策略
        strategy = self.optimization_model.predict(features)

        # 应用优化
        return apply_optimizations(graph, strategy)

原始迭代空间：
for i = 0 to N-1
    for j = 0 to M-1
        A[i][j] = B[i][j] + C[i][j]

多面体表示：
Domain: {[i,j] : 0 ≤ i < N ∧ 0 ≤ j < M}
Schedule: θ(i,j) = (i,j)  // 执行顺序
Access: A[i][j], B[i][j], C[i][j]

# ISL多面体优化
def polyhedral_optimize(loop_nest):
    # 构建多面体表示
    domain = build_iteration_domain(loop_nest)
    deps = extract_dependencies(loop_nest)

    # 优化目标：最小化cache miss
    objective = minimize_cache_misses(domain, deps)

    # 求解最优调度
    schedule = isl.schedule_constraints_compute_schedule(
        domain, deps, objective
    )

    # 生成优化代码
    return codegen_from_schedule(schedule)

// 原始代码：差的局部性
for (i = 0; i < N; i++)
    for (j = 0; j < N; j++)
        for (k = 0; k < N; k++)
            C[i][j] += A[i][k] * B[k][j];

// Polyhedral优化后：tiling + 交换
for (ii = 0; ii < N; ii += TILE)
    for (jj = 0; jj < N; jj += TILE)
        for (kk = 0; kk < N; kk += TILE)
            for (i = ii; i < min(ii+TILE, N); i++)
                for (k = kk; k < min(kk+TILE, N); k++)
                    for (j = jj; j < min(jj+TILE, N); j++)
                        C[i][j] += A[i][k] * B[k][j];

# Halide风格的调度语言
def generate_optimized_conv(params):
    # 定义计算
    conv = define_convolution(params)

    # 调度优化
    conv.compute_root()
    conv.tile(x, y, xi, yi, 32, 32)
    conv.vectorize(xi, 8)
    conv.parallel(y)
    conv.unroll(c)

    # 生成目标代码
    if params.target == 'arm':
        return generate_neon_code(conv)
    elif params.target == 'x86':
        return generate_avx_code(conv)

# AutoTVM风格的自动调优
class AutoScheduler:
    def __init__(self, target_hw):
        self.target = target_hw
        self.cost_model = XGBoostCostModel()

    def search(self, workload, num_trials=1000):
        space = self.define_search_space(workload)

        for trial in range(num_trials):
            # 采样配置
            config = self.sample_configuration(space)

            # 评估性能
            latency, energy = self.measure(config)

            # 更新模型
            self.cost_model.update(config, latency, energy)

            # 引导搜索
            space = self.prune_space(space, self.cost_model)

        return self.best_config

# 低功耗AI DSL示例
@dsl.kernel
def optimized_matmul(A: T.tensor, B: T.tensor) -> T.tensor:
    # 声明计算
    C = T.compute(
        shape=(A.shape[0], B.shape[1]),
        fcompute=lambda i, j: T.sum(A[i, k] * B[k, j], axis=k)
    )

    # 功耗优化标注
    with T.power_budget(10):  # 10W功耗预算
        # 自动选择最优实现
        if T.is_sparse(A) > 0.9:
            return sparse_matmul(A, B)
        elif T.data_type(A) == 'int8':
            return quantized_matmul(A, B)
        else:
            return dense_matmul(A, B)

// CUDA统一内存示例
class UnifiedMemoryManager {
public:
    void* allocate(size_t size, DeviceType preferred) {
        void* ptr;

        if (preferred == GPU) {
            // GPU优先，按需迁移到CPU
            cudaMallocManaged(&ptr, size);
            cudaMemAdvise(ptr, size, cudaMemAdviseSetPreferredLocation, 0);
        } else {
            // CPU优先，按需迁移到GPU
            cudaMallocManaged(&ptr, size);
            cudaMemPrefetchAsync(ptr, size, cudaCpuDeviceId);
        }

        return ptr;
    }

    void migrate(void* ptr, size_t size, DeviceType target) {
        int device = (target == GPU) ? 0 : cudaCpuDeviceId;
        cudaMemPrefetchAsync(ptr, size, device);
    }
};

# CPU-GPU协同执行
class HeterogeneousScheduler:
    def __init__(self):
        self.cpu_queue = Queue()
        self.gpu_queue = Queue()
        self.profiler = PowerProfiler()

    def schedule_layer(self, layer, input):
        # 预测执行时间和功耗
        cpu_time, cpu_power = self.profiler.predict_cpu(layer)
        gpu_time, gpu_power = self.profiler.predict_gpu(layer)

        # 考虑数据传输开销
        transfer_cost = self.estimate_transfer_cost(input)

        # 选择最优设备
        if self.power_constrained:
            if cpu_power < gpu_power - transfer_cost:
                return self.execute_on_cpu(layer, input)
        else:
            if gpu_time + transfer_cost < cpu_time:
                return self.execute_on_gpu(layer, input)

# 多设备流水线
class PipelineOptimizer:
    def optimize_pipeline(self, model, devices):
        # 模型分割
        stages = self.partition_model(model, len(devices))

        # 分配到设备
        mapping = {}
        for i, (stage, device) in enumerate(zip(stages, devices)):
            # 考虑通信和计算平衡
            compute_cost = self.estimate_compute(stage, device)
            comm_cost = self.estimate_communication(stage, i)

            # 优化分配
            if compute_cost > comm_cost * 2:
                # 计算密集，可能需要分割
                substages = self.split_stage(stage)
                mapping[device] = substages
            else:
                mapping[device] = [stage]

        return self.generate_pipeline_schedule(mapping)

class EnergyAwareJIT:
    def __init__(self):
        self.profile_data = {}
        self.energy_model = EnergyModel()

    def compile(self, function, inputs):
        # 收集profile信息
        if function not in self.profile_data:
            self.profile_data[function] = self.profile(function, inputs)

        profile = self.profile_data[function]

        # 基于能效选择优化
        if profile.is_memory_bound:
            return self.optimize_for_memory(function)
        elif profile.has_high_sparsity:
            return self.optimize_for_sparsity(function)
        else:
            return self.optimize_for_compute(function)

    def adaptive_recompile(self, function, new_profile):
        # 检测执行模式变化
        if self.pattern_changed(new_profile):
            # 触发重编译
            return self.compile(function, new_profile.inputs)
        return None

// 运行时代码生成
class DynamicSpecializer {
public:
    typedef void (*KernelFunc)(float*, float*, int);

    KernelFunc specialize(int size, float sparsity) {
        std::string key = std::to_string(size) + "_" + std::to_string(sparsity);

        if (cache.find(key) != cache.end()) {
            return cache[key];
        }

        // 生成特化代码
        std::string code = generate_specialized_kernel(size, sparsity);

        // JIT编译
        KernelFunc kernel = jit_compile(code);
        cache[key] = kernel;

        return kernel;
    }

private:
    std::map<std::string, KernelFunc> cache;
};

class OnlineLearningOptimizer:
    def __init__(self):
        self.bandits = {}  # 多臂老虎机

    def select_optimization(self, context):
        if context not in self.bandits:
            self.bandits[context] = MultiArmedBandit()

        # 选择优化策略
        strategy = self.bandits[context].select_arm()

        # 执行并测量
        result = self.execute_with_strategy(strategy)

        # 更新奖励
        reward = -result.energy  # 最小化能耗
        self.bandits[context].update(strategy, reward)

        return result

# 渐进式优化
class ProgressiveOptimizer:
    def __init__(self):
        self.optimization_levels = [
            'O0',  # 无优化
            'O1',  # 基础优化
            'O2',  # 激进优化
            'Os',  # 优化大小
            'Op'   # 优化功耗
        ]

    def optimize_incrementally(self, module):
        best_config = None
        best_efficiency = 0

        for level in self.optimization_levels:
            # 编译当前优化级别
            compiled = self.compile(module, level)

            # 评估能效
            efficiency = self.measure_efficiency(compiled)

            if efficiency > best_efficiency:
                best_efficiency = efficiency
                best_config = compiled
            elif efficiency < best_efficiency * 0.9:
                # 性能退化，停止
                break

        return best_config

# 错误：融合导致寄存器溢出
fused_op = fuse_all([conv1, conv2, conv3, conv4, conv5])
# 寄存器不足，反而增加内存访问

# 考虑寄存器压力
if estimate_register_pressure(ops) < available_registers:
    fused_op = fuse(ops)

# 错误：忽略CPU-GPU传输
gpu_result = gpu_compute(data)  # 忽略数据传输时间

transfer_time = measure_transfer(data.size)
compute_time = estimate_compute(data)
if compute_time > transfer_time * 2:  # 值得使用GPU
    gpu_result = gpu_compute(data)

# 错误：硬编码批大小
optimizer.batch_size = 32  # 固定值

# 根据队列长度动态调整
batch_size = min(queue.length, max_batch)
batch_size = adjust_for_latency(batch_size, target_latency)