Meta Iris自研AI芯片：三星2nm代工、6周通过测试、7GW→14GW算力翻倍，MTIA四代路线图的工程密码

"""
Meta Iris Chip Architecture Analysis
"""

class ChipletModule:
    """Chiplet模块化架构的核心单元"""
    
    def __init__(self, name: str, compute_tflops: float, 
                 memory_gb: float, power_w: float):
        self.name = name
        self.compute = compute_tflops  # TFLOPS (FP8)
        self.memory = memory_gb
        self.power = power_w
    
    def efficiency(self) -> float:
        """能效比：TFLOPS/W"""
        return self.compute / self.power


class IrisArchitecture:
    """
    Iris芯片架构模拟
    
    Meta采用模块化Chiplet设计，每个chiplet可根据需求组合。
    这种设计允许Meta根据AI工作负载的快速变化灵活调整。
    """
    
    def __init__(self):
        # 基础计算单元
        self.compute_chiplet = ChipletModule(
            "Compute Chiplet", 500, 0, 120
        )
        # 内存单元
        self.memory_chiplet = ChipletModule(
            "Memory Chiplet (HBM3e)", 0, 32, 15
        )
        # IO/互联单元
        self.io_chiplet = ChipletModule(
            "IO Chiplet", 0, 0, 10
        )
    
    def build_configuration(self, compute_chiplets: int, 
                            memory_chiplets: int,
                            io_chiplets: int) -> dict:
        """构建特定配置的芯片"""
        total_compute = self.compute_chiplet.compute * compute_chiplets
        total_memory = self.memory_chiplet.memory * memory_chiplets
        total_power = (
            self.compute_chiplet.power * compute_chiplets +
            self.memory_chiplet.power * memory_chiplets +
            self.io_chiplet.power * io_chiplets
        )
        
        return {
            "config": f"{compute_chiplets}C+{memory_chiplets}M+{io_chiplets}IO",
            "compute_tflops": total_compute,
            "memory_gb": total_memory,
            "power_watts": total_power,
            "efficiency": total_compute / total_power if total_power > 0 else 0,
        }
    
    def generate_configurations(self) -> list:
        """生成不同配置方案"""
        configs = [
            self.build_configuration(4, 6, 2),  # 标准推理
            self.build_configuration(8, 12, 4),  # 高性能推理
            self.build_configuration(16, 24, 8), # 训练
        ]
        return configs


class MetaComputePlan:
    """
    Meta 7GW→14GW算力规划
    
    2026年：7GW总算力
    2027年：14GW总算力（翻倍）
    AI基础设施投资：~$1450亿
    """
    
    def __init__(self):
        self.year_2026 = {
            "total_gw": 7.0,
            "nvidia_gpu_pct": 0.60,
            "meta_chip_pct": 0.25,
            "other_pct": 0.15,
            "investment_billion": 145,
        }
        self.year_2027 = {
            "total_gw": 14.0,
            "nvidia_gpu_pct": 0.40,
            "meta_chip_pct": 0.40,
            "other_pct": 0.20,
            "investment_billion": 180,
        }
    
    def compute_breakdown(self, year_data: dict) -> dict:
        """计算各供应商的算力占比"""
        total_gw = year_data["total_gw"]
        return {
            "total_gw": total_gw,
            "nvidia_gpu": total_gw * year_data["nvidia_gpu_pct"],
            "meta_chip": total_gw * year_data["meta_chip_pct"],
            "other": total_gw * year_data["other_pct"],
            "investment": year_data["investment_billion"],
        }
    
    def chip_dependency_reduction(self) -> float:
        """计算Meta芯片对NVIDIA依赖的降低幅度"""
        dep_2026 = self.year_2026["nvidia_gpu_pct"]
        dep_2027 = self.year_2027["nvidia_gpu_pct"]
        return (dep_2026 - dep_2027) / dep_2026 * 100


class SupplyChainAgreement:
    """Meta的长期供应协议"""
    
    def __init__(self):
        self.agreements = [
            {
                "partner": "Samsung Electronics",
                "product": "Memory chips (HBM3e, DDR5)",
                "term": "Multi-year",
                "purpose": "AI accelerator memory",
            },
            {
                "partner": "SanDisk",
                "product": "Flash storage",
                "term": "Multi-year",
                "purpose": "Data center storage",
            },
            {
                "partner": "Sumitomo Electric",
                "product": "Fiber optic equipment",
                "term": "Multi-year",
                "purpose": "Data center interconnect",
            },
            {
                "partner": "Broadcom",
                "product": "Chip design (co-design)",
                "term": "Ongoing",
                "purpose": "Iris chip physical design",
            },
            {
                "partner": "TSMC",
                "product": "Manufacturing",
                "term": "Per-project",
                "purpose": "Iris chip fabrication",
            },
        ]


if __name__ == "__main__":
    # Iris架构分析
    iris = IrisArchitecture()
    configs = iris.generate_configurations()
    
    print("=" * 60)
    print("Meta Iris Chip Configuration Analysis")
    print("=" * 60)
    for cfg in configs:
        print(f"\nConfig {cfg['config']}:")
        print(f"  Compute: {cfg['compute_tflops']} TFLOPS")
        print(f"  Memory:  {cfg['memory_gb']} GB HBM3e")
        print(f"  Power:   {cfg['power_watts']} W")
        print(f"  Eff:     {cfg['efficiency']:.1f} TFLOPS/W")
    
    # Meta算力规划
    plan = MetaComputePlan()
    print(f"\n{'=' * 60}")
    print("Meta Compute Capacity Plan")
    print(f"{'=' * 60}")
    
    for year_str, year_data in [("2026", plan.year_2026), 
                                 ("2027", plan.year_2027)]:
        breakdown = plan.compute_breakdown(year_data)
        print(f"\n{year_str}: {breakdown['total_gw']}GW total")
        print(f"  NVIDIA: {breakdown['nvidia_gpu']:.2f}GW "
              f"({year_data['nvidia_gpu_pct']*100:.0f}%)")
        print(f"  Meta:   {breakdown['meta_chip']:.2f}GW "
              f"({year_data['meta_chip_pct']*100:.0f}%)")
        print(f"  Other:  {breakdown['other']:.2f}GW "
              f"({year_data['other_pct']*100:.0f}%)")
        print(f"  Investment: ${breakdown['investment']}B")
    
    print(f"\nNVIDIA dependency reduction: "
          f"{plan.chip_dependency_reduction():.0f}%")

// Go实现：芯片测试流程与周期对比
package main

import (
    "fmt"
    "strings"
)

type ChipTestPhase struct {
    Name           string
    TypicalWeeks   int
    IrisWeeks      int
    Criticality    string
}

func main() {
    phases := []ChipTestPhase{
        {"Power-on Test (POST)", 2, 0.5, "Critical"},
        {"Functional Validation", 8, 1.5, "Critical"},
        {"Performance Characterization", 6, 1, "High"},
        {"Power/Thermal Validation", 4, 1, "High"},
        {"Memory Subsystem Test", 4, 0.5, "Critical"},
        {"IO Interface Validation", 3, 0.5, "High"},
        {"Stress Testing (48h+ burn-in)", 2, 0.5, "Medium"},
        {"Software Stack Integration", 8, 0.5, "Critical"},
    }
    
    totalTypical := 0
    totalIris := 0.0
    
    fmt.Println(strings.Repeat("=", 70))
    fmt.Println("Chip Test Cycle Comparison: Industry vs Meta Iris")
    fmt.Println(strings.Repeat("=", 70))
    fmt.Printf("%-30s %8s %8s %s\n", "Phase", "Typical", "Iris", "Criticality")
    fmt.Println(strings.Repeat("-", 70))
    
    for _, p := range phases {
        totalTypical += p.TypicalWeeks
        totalIris += p.IrisWeeks
        fmt.Printf("%-30s %8dw %8.1fw %s\n", p.Name, 
                   p.TypicalWeeks, p.IrisWeeks, p.Criticality)
    }
    
    fmt.Println(strings.Repeat("-", 70))
    fmt.Printf("%-30s %8dw %8.1fw\n", "TOTAL", totalTypical, totalIris)
    fmt.Printf("\nTime savings: %d weeks → %.1f weeks (%.0f%% reduction)\n",
               totalTypical, totalIris, 
               (1 - totalIris/float64(totalTypical))*100)
    
    fmt.Println("\n" + strings.Repeat("=", 70))
    fmt.Println("Key Implications:")
    fmt.Println("  1. Meta reused significant IP from prior MTIA generations")
    fmt.Println("  2. The chiplet architecture enables per-module validation")
    fmt.Println("  3. Broadcom's physical design expertise reduced tapeout risk")
    fmt.Println("  4. TSMC's 2nm process maturity exceeded expectations")
    fmt.Println("=" + strings.Repeat("=", 70))
}