巴萨BA-3000动态稀疏数据流推理芯片深度解析：12.8 TOPS/W能效比如何改写中国AI推理芯片格局

import numpy as np
import time
from typing import List, Tuple

class DynamicSparseComputeUnit:
    """BA-3000动态稀疏感知计算单元模拟"""
    
    def __init__(self, mac_count: int = 256):
        self.mac_count = mac_count
        self.macs_busy = 0
        self.total_cycles = 0
        self.skipped_cycles = 0
    
    def sparse_matmul(self, A: np.ndarray, B: np.ndarray) -> np.ndarray:
        """
        模拟动态稀疏感知矩阵乘法
        核心逻辑：实时检测输入稀疏性，跳过零值MAC操作
        """
        m, k = A.shape
        k, n = B.shape
        C = np.zeros((m, n), dtype=np.float32)
        
        start_time = time.time()
        
        for i in range(m):
            for j in range(n):
                acc = 0.0
                for p in range(k):
                    self.total_cycles += 1
                    a_val = A[i, p]
                    b_val = B[p, j]
                    
                    # 零值跳过：两个操作数任一个为零，直接跳过
                    if abs(a_val) < 1e-10 or abs(b_val) < 1e-10:
                        self.skipped_cycles += 1
                        continue
                    
                    # 非零值才执行MAC操作
                    acc += a_val * b_val
                    self.macs_busy += 1
                
                C[i, j] = acc
        
        elapsed = time.time() - start_time
        sparsity = (A.size - np.count_nonzero(A)) / A.size
        skip_rate = self.skipped_cycles / self.total_cycles if self.total_cycles > 0 else 0
        
        print(f"[DSCU] 矩阵尺寸: {m}x{k} x {k}x{n}")
        print(f"[DSCU] 输入稀疏度: {sparsity:.2%}")
        print(f"[DSCU] 零值跳过率: {skip_rate:.2%}")
        print(f"[DSCU] 有效MAC利用率: {self.macs_busy / self.total_cycles:.2%}")
        print(f"[DSCU] 执行耗时: {elapsed*1000:.2f}ms")
        
        return C

# 模拟：构建一个稀疏权重矩阵（剪枝后，80%稀疏）
np.random.seed(42)
A = np.random.randn(512, 768)
# 应用80%稀疏性（模拟剪枝后的权重）
threshold = np.percentile(np.abs(A), 80)
A[np.abs(A) < threshold] = 0.0

B = np.random.randn(768, 256)

# 执行动态稀疏计算
dscu = DynamicSparseComputeUnit(mac_count=256)
result = dscu.sparse_matmul(A, B)

print(f"\n等效MAC吞吐: {dscu.macs_busy / dscu.total_cycles * 256:.1f} MACs/cycle")

class NearMemoryComputeMacro:
    """BA-3000近存计算宏单元模拟"""
    
    def __init__(self, sram_size_kb: int = 256, logic_distance_um: float = 1.8):
        self.sram_size = sram_size_kb * 1024  # bytes
        self.logic_distance = logic_distance_um  # 微米
        self.energy_per_bit_per_um = 0.5  # fJ/bit/μm (7nm工艺估算)
    
    def compute_energy_saving(self, traditional_distance_um: float = 80.0) -> dict:
        """
        计算近存架构相对于传统分离方案的能效收益
        """
        bits_per_access = 256 * 8  # 256位总线宽度
        access_count = 1_000_000  # 模拟100万次内存访问
        
        # 传统方案：数据走线距离远
        traditional_energy = (
            bits_per_access * access_count * 
            self.energy_per_bit_per_um * traditional_distance_um
        )
        
        # BA-3000近存方案：数据走线距离<2μm
        ba3000_energy = (
            bits_per_access * access_count * 
            self.energy_per_bit_per_um * self.logic_distance
        )
        
        saving_ratio = traditional_energy / ba3000_energy
        
        return {
            "traditional_energy_pJ": traditional_energy,
            "ba3000_energy_pJ": ba3000_energy,
            "saving_ratio": saving_ratio,
            "distance_reduction": f"{traditional_distance_um/self.logic_distance:.0f}x"
        }

macro = NearMemoryComputeMacro()
energy_report = macro.compute_energy_saving()
print(f"=== 近存计算能效分析 ===")
print(f"SRAM-逻辑间距: {macro.logic_distance}μm")
print(f"传统方案间距: 80μm")
print(f"传统方案能耗: {energy_report['traditional_energy_pJ']:.2e} pJ")
print(f"BA-3000能耗:  {energy_report['ba3000_energy_pJ']:.2e} pJ")
print(f"能效提升:     {energy_report['saving_ratio']:.0f}x")
print(f"走线距离缩减: {energy_report['distance_reduction']}")

// BA-3000 RISC-V V-EXT 指令集模拟（Go语言）
package main

import (
	"fmt"
	"math"
	"math/rand"
	"time"
)

// VExtInstruction 模拟BA-3000的V-EXT向量扩展指令
type VExtInstruction struct {
	name      string
	latency   int // 时钟周期数
	throughput float64 // 每时钟周期处理的操作数
}

// VExtEngine V-EXT执行引擎
type VExtEngine struct {
	instructions map[string]VExtInstruction
	vectorLen    int // 向量长度（VLEN）
}

func NewVExtEngine(vectorLen int) *VExtEngine {
	eng := &VExtEngine{
		vectorLen: vectorLen,
		instructions: make(map[string]VExtInstruction),
	}
	
	// 注册V-EXT指令集
	eng.instructions["vmatmul.s"] = VExtInstruction{"稀疏矩阵-向量乘", 8, 64.0}
	eng.instructions["vconv2d.d"] = VExtInstruction{"二维卷积", 12, 32.0}
	eng.instructions["vlayernorm.s"] = VExtInstruction{"LayerNorm", 4, 128.0}
	eng.instructions["vattention.s"] = VExtInstruction{"自注意力", 16, 16.0}
	eng.instructions["vgather.s"] = VExtInstruction{"稀疏Gather", 2, 256.0}
	eng.instructions["vscatter.s"] = VExtInstruction{"稀疏Scatter", 2, 256.0}
	
	return eng
}

// SparseMatMul 使用vmatmul.s指令实现稀疏矩阵-向量乘
func (e *VExtEngine) SparseMatMul(
	values []float32,      // 非零值
	colIndices []int32,    // 列索引
	rowPtr []int32,        // 行指针（CSR格式）
	x []float32,           // 输入向量
) []float32 {
	n := len(rowPtr) - 1
	y := make([]float32, n)
	
	inst := e.instructions["vmatmul.s"]
	
	for i := 0; i < n; i++ {
		start := rowPtr[i]
		end := rowPtr[i+1]
		var acc float32 = 0.0
		
		// 模拟vmatmul.s的向量化执行
		for j := start; j < end; j++ {
			col := colIndices[j]
			val := values[j]
			acc += val * x[col]
		}
		
		y[i] = acc
	}
	
	vecOps := float64(len(values))
	cycles := vecOps / inst.throughput
	fmt.Printf("[vmatmul.s] 处理 %d 个非零元素, 耗时 %.1f 周期, 吞吐 %.1f ops/cycle\n",
		len(values), cycles, inst.throughput)
	
	return y
}

// Attention 使用vattention.s实现自注意力机制
func (e *VExtEngine) Attention(Q, K, V []float32, dim int) []float32 {
	seqLen := len(Q) / dim
	output := make([]float32, seqLen*dim)
	
	inst := e.instructions["vattention.s"]
	
	// 模拟vattention.s的硬件加速
	// 包含：QK^T矩阵乘 + softmax + 加权求和
	for i := 0; i < seqLen; i++ {
		qi := Q[i*dim : (i+1)*dim]
		
		// 计算注意力分数（硬件加速）
		scores := make([]float32, seqLen)
		for j := 0; j < seqLen; j++ {
			kj := K[j*dim : (j+1)*dim]
			var dot float32 = 0.0
			for d := 0; d < dim; d++ {
				dot += qi[d] * kj[d]
			}
			scores[j] = dot / float32(math.Sqrt(float64(dim)))
		}
		
		// Softmax（硬件实现）
		var maxVal float32 = scores[0]
		for _, s := range scores {
			if s > maxVal {
				maxVal = s
			}
		}
		var sum float32 = 0.0
		expScores := make([]float32, seqLen)
		for j, s := range scores {
			expScores[j] = float32(math.Exp(float64(s - maxVal)))
			sum += expScores[j]
		}
		for j := range expScores {
			expScores[j] /= sum
		}
		
		// 加权求和
		oi := output[i*dim : (i+1)*dim]
		for d := 0; d < dim; d++ {
			var weighted float32 = 0.0
			for j := 0; j < seqLen; j++ {
				weighted += expScores[j] * V[j*dim+d]
			}
			oi[d] = weighted
		}
	}
	
	vecOps := float64(seqLen * seqLen * dim * 2)
	cycles := vecOps / inst.throughput
	fmt.Printf("[vattention.s] seq_len=%d, dim=%d, 处理 %d 个向量操作, 耗时 %.1f 周期\n",
		seqLen, dim, int(vecOps), cycles)
	
	return output
}

func main() {
	rand.Seed(time.Now().UnixNano())
	
	eng := NewVExtEngine(512)
	fmt.Println("=== BA-3000 V-EXT 指令集模拟 ===")
	fmt.Printf("向量长度(VLEN): %d\n\n", eng.vectorLen)
	
	// 测试1: 稀疏矩阵-向量乘
	fmt.Println("--- 测试1: vmatmul.s 稀疏矩阵-向量乘 ---")
	// 构建一个1000x1000的稀疏矩阵，稀疏度95%
	n := 1000
	nnz := n * n * 5 / 100 // 5%非零
	values := make([]float32, nnz)
	colIndices := make([]int32, nnz)
	rowPtr := make([]int32, n+1)
	
	for i := 0; i < nnz; i++ {
		values[i] = rand.Float32()
		colIndices[i] = int32(rand.Intn(n))
	}
	// 简单分配行指针
	for i := 0; i <= n; i++ {
		rowPtr[i] = int32(i * nnz / n)
	}
	
	x := make([]float32, n)
	for i := range x {
		x[i] = rand.Float32()
	}
	
	result := eng.SparseMatMul(values, colIndices, rowPtr, x)
	fmt.Printf("输出向量维度: %d\n\n", len(result))
	
	// 测试2: 自注意力机制
	fmt.Println("--- 测试2: vattention.s 自注意力 ---")
	seqLen := 128
	dim := 64
	q := make([]float32, seqLen*dim)
	k := make([]float32, seqLen*dim)
	v := make([]float32, seqLen*dim)
	for i := range q {
		q[i] = rand.Float32()
		k[i] = rand.Float32()
		v[i] = rand.Float32()
	}
	
	attnOut := eng.Attention(q, k, v, dim)
	fmt.Printf("注意力输出维度: %d\n", len(attnOut))
	
	// 性能对比：V-EXT vs 纯软件实现
	fmt.Println("\n--- 性能对比: V-EXT vs 纯软件 ---")
	softOps := float64(nnz) // 稀疏矩阵-向量乘的操作数
	vextCycles := softOps / eng.instructions["vmatmul.s"].throughput
	softCycles := softOps * 4 // 纯软件需要更多周期（指令获取+解码+数据搬运）
	
	fmt.Printf("稀疏矩阵-向量乘:\n")
	fmt.Printf("  操作数: %.0f\n", softOps)
	fmt.Printf("  V-EXT周期: %.0f\n", vextCycles)
	fmt.Printf("  纯软件周期: %.0f\n", softCycles)
	fmt.Printf("  加速比: %.1fx\n", softCycles/vextCycles)
}

class YieldModel:
    """BA-3000良率分析与产能估算"""
    
    def __init__(self, die_area_mm2: float, defect_density: float, 
                 wafer_diameter_mm: float = 300):
        self.die_area = die_area_mm2
        self.defect_density = defect_density  # defects/cm²
        self.wafer_area = np.pi * (wafer_diameter_mm / 2) ** 2
    
    def compute_yield(self, alpha: float = 1.0) -> float:
        """使用负二项分布模型计算良率"""
        # Y = (1 + A * D / alpha)^(-alpha)
        area_cm2 = self.die_area / 100  # mm² -> cm²
        yield_rate = (1 + area_cm2 * self.defect_density / alpha) ** (-alpha)
        return yield_rate
    
    def dies_per_wafer(self) -> int:
        """估算每片晶圆可切割的Die数量"""
        # 使用近似公式：N = π * (R - sqrt(A))² / A
        R = np.sqrt(self.wafer_area / np.pi)
        effective_R = R - np.sqrt(self.die_area)  # 扣除边缘损失
        return int(np.pi * effective_R**2 / self.die_area)
    
    def production_analysis(self, wafers: int, stages: list) -> dict:
        """多阶段量产分析"""
        results = []
        for stage in stages:
            y = self.compute_yield(alpha=stage['alpha'])
            dpw = self.dies_per_wafer()
            good_dies = int(dpw * y * stage['wafers'])
            results.append({
                'stage': stage['name'],
                'wafers': stage['wafers'],
                'yield': y,
                'dies_per_wafer': dpw,
                'good_dies': good_dies
            })
        return results

# 模拟BA-3000量产
import numpy as np
ym = YieldModel(die_area_mm2=350, defect_density=0.08)

stages = [
    {'name': '首批（2026Q1）', 'wafers': 10000, 'alpha': 1.0},
    {'name': '爬坡（2026Q2）', 'wafers': 25000, 'alpha': 1.2},
    {'name': '满产（2026Q3）', 'wafers': 50000, 'alpha': 1.5},
]

results = ym.production_analysis(10000, stages)
print("=== BA-3000量产分析 ===")
print(f"Die面积: {ym.die_area}mm²")
print(f"缺陷密度: {ym.defect_density} defects/cm²")
print(f"晶圆直径: 300mm\n")

for r in results:
    print(f"{r['stage']}:")
    print(f"  晶圆数: {r['wafers']:,}")
    print(f"  良率: {r['yield']:.1%}")
    print(f"  每片Die数: {r['dies_per_wafer']}")
    print(f"  良品Die数: {r['good_dies']:,}")
    print()

import torch
import torch.nn as nn
import numpy as np
from typing import Optional, Tuple

# ============================================================
# BA-3000推理模拟：LLaMA-7B在BA-3000上的部署
# 展示动态稀疏数据流架构如何加速大模型推理
# ============================================================

class SparseLinear(nn.Module):
    """BA-3000稀疏线性层模拟"""
    
    def __init__(self, in_features: int, out_features: int, sparsity: float = 0.7):
        super().__init__()
        self.in_features = in_features
        self.out_features = out_features
        self.sparsity = sparsity
        
        # 初始化权重并应用结构化剪枝
        weight = torch.randn(out_features, in_features)
        self.register_buffer('weight', weight)
        
        # 模拟BA-3000的硬件稀疏存储
        self._apply_hardware_sparsity()
    
    def _apply_hardware_sparsity(self):
        """模拟BA-3000的硬件稀疏存储格式"""
        weight = self.weight.clone()
        
        # 应用N:M结构化稀疏（BA-3000支持2:4和4:8模式）
        # 这里使用2:4模式：每4个元素保留2个最大的
        with torch.no_grad():
            # 重塑为 (N, 4) 每组处理
            flat = weight.view(-1, 4)
            _, indices = torch.topk(torch.abs(flat), k=2, dim=1)
            mask = torch.zeros_like(flat)
            mask.scatter_(1, indices, 1.0)
            weight = (flat * mask).view_as(weight)
        
        self.weight = weight
    
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """
        BA-3000的稀疏线性层前向传播
        - 硬件自动跳过零值权重
        - 近存计算减少数据搬运
        """
        # 模拟BA-3000的零值跳过
        weight_sparsity = (self.weight == 0).float().mean().item()
        
        # 模拟硬件加速：只计算非零权重
        start = torch.cuda.Event(enable_timing=True)
        end = torch.cuda.Event(enable_timing=True)
        
        # 正常计算（模拟BA-3000硬件加速后的结果）
        output = torch.nn.functional.linear(x, self.weight)
        
        return output, weight_sparsity


class BA3000LLaMABlock(nn.Module):
    """BA-3000优化的LLaMA解码块"""
    
    def __init__(self, dim: int, n_heads: int, sparsity: float = 0.7):
        super().__init__()
        self.dim = dim
        self.n_heads = n_heads
        self.head_dim = dim // n_heads
        
        # BA-3000 V-EXT加速的注意力机制
        self.wq = SparseLinear(dim, dim, sparsity)
        self.wk = SparseLinear(dim, dim, sparsity)
        self.wv = SparseLinear(dim, dim, sparsity)
        self.wo = SparseLinear(dim, dim, sparsity)
        
        # BA-3000 V-EXT加速的FFN
        self.w1 = SparseLinear(dim, dim * 4, sparsity)
        self.w2 = SparseLinear(dim * 4, dim, sparsity)
        self.w3 = SparseLinear(dim, dim * 4, sparsity)
        
        # BA-3000 V-EXT加速的归一化层（vlayernorm.s）
        self.norm1 = nn.LayerNorm(dim)
        self.norm2 = nn.LayerNorm(dim)
    
    def forward(self, x: torch.Tensor, 
                mask: Optional[torch.Tensor] = None) -> Tuple[torch.Tensor, dict]:
        stats = {}
        
        # 注意力前归一化（vlayernorm.s加速）
        h = self.norm1(x)
        
        # QKV投影（BASA Compiler自动融合为单个vmatmul.s操作）
        q, q_sparsity = self.wq(h)
        k, k_sparsity = self.wk(h)
        v, v_sparsity = self.wv(h)
        
        stats['qkv_sparsity'] = (q_sparsity + k_sparsity + v_sparsity) / 3
        
        # 多头注意力（vattention.s加速）
        B, L, D = q.shape
        q = q.view(B, L, self.n_heads, self.head_dim).transpose(1, 2)
        k = k.view(B, L, self.n_heads, self.head_dim).transpose(1, 2)
        v = v.view(B, L, self.n_heads, self.head_dim).transpose(1, 2)
        
        # 硬件加速的注意力计算
        attn = (q @ k.transpose(-2, -1)) / (self.head_dim ** 0.5)
        if mask is not None:
            attn = attn + mask
        attn = torch.softmax(attn, dim=-1)
        out = (attn @ v).transpose(1, 2).reshape(B, L, D)
        
        # 输出投影
        out, _ = self.wo(out)
        
        # 残差连接
        h = x + out
        
        # FFN前归一化
        h2 = self.norm2(h)
        
        # SwiGLU FFN（BA-3000多指令并行发射）
        w1_out, w1_sparsity = self.w1(h2)
        w3_out, w3_sparsity = self.w3(h2)
        gate = torch.silu(w1_out) * w3_out
        out2, _ = self.w2(gate)
        
        stats['ffn_sparsity'] = (w1_sparsity + w3_sparsity) / 2
        stats['total_sparsity'] = (stats['qkv_sparsity'] + stats['ffn_sparsity']) / 2
        
        return h + out2, stats


class BA3000LLaMA(nn.Module):
    """BA-3000上部署的LLaMA-7B完整模型"""
    
    def __init__(self, vocab_size: int = 32000, dim: int = 4096, 
                 n_layers: int = 32, n_heads: int = 32):
        super().__init__()
        self.tok_embeddings = nn.Embedding(vocab_size, dim)
        
        self.layers = nn.ModuleList([
            BA3000LLaMABlock(dim, n_heads, sparsity=0.7)
            for _ in range(n_layers)
        ])
        
        self.norm = nn.LayerNorm(dim)
        self.output = nn.Linear(dim, vocab_size, bias=False)
    
    @torch.no_grad()
    def forward(self, tokens: torch.Tensor) -> Tuple[torch.Tensor, dict]:
        h = self.tok_embeddings(tokens)
        
        all_stats = []
        for layer in self.layers:
            h, stats = layer(h)
            all_stats.append(stats)
        
        h = self.norm(h)
        logits = self.output(h)
        
        # 汇总统计
        avg_sparsity = np.mean([s['total_sparsity'] for s in all_stats])
        
        return logits, {'avg_sparsity': avg_sparsity}

# 模拟推理
print("=== BA-3000 LLaMA-7B推理模拟 ===")
model = BA3000LLaMA()
dummy_input = torch.randint(0, 1000, (1, 128))
logits, stats = model(dummy_input)
print(f"输入序列长度: 128")
print(f"输出logits维度: {list(logits.shape)}")
print(f"平均权重稀疏度: {stats['avg_sparsity']:.2%}")
print(f"有效算力利用率: {(1 - stats['avg_sparsity'])*100:.1f}%")

class BASARuntime:
    """BA-3000运行时调度器模拟"""
    
    def __init__(self, chip_count: int = 1):
        self.chip_count = chip_count
        self.chip_power = 30.0  # Watts per chip
        self.total_power = chip_count * self.chip_power
    
    def estimate_latency(self, model_params: dict, 
                         batch_size: int = 1,
                         input_length: int = 128,
                         output_length: int = 32) -> dict:
        """
        估算BA-3000上大模型推理延迟
        基于BA-3000的硬件参数：384 TOPS INT8, 4.8 TB/s HBM3e
        """
        # 模型参数
        n_layers = model_params.get('n_layers', 32)
        dim = model_params.get('dim', 4096)
        n_heads = model_params.get('n_heads', 32)
        vocab_size = model_params.get('vocab_size', 32000)
        ffn_dim = model_params.get('ffn_dim', dim * 4)
        
        # BA-3000硬件参数
        peak_ops = 384e12  # 384 TOPS INT8
        hbm_bandwidth = 4.8e12  # 4.8 TB/s
        sparsity_benefit = 0.7  # 70%稀疏度带来的加速
        
        # 预填充阶段（Prefill）：并行处理输入token
        prefill_ops = 0
        prefill_ops += 2 * n_layers * (4 * dim * dim)  # QKV + O投影
        prefill_ops += 2 * n_layers * (dim * ffn_dim * 3)  # FFN
        prefill_ops += n_layers * (2 * n_heads * input_length * input_length * dim // n_heads)  # 注意力
        
        # 考虑稀疏加速
        prefill_ops *= (1 - sparsity_benefit * 0.7)  # 70%稀疏度，硬件跳过70%的零值
        
        # 解码阶段（Decoding）：自回归生成
        decode_ops = prefill_ops / input_length * output_length
        
        # 内存访问估算
        param_size = 2 * n_layers * (4 * dim * dim + 2 * dim * ffn_dim)  # 参数量（FP16）
        kv_cache_size = 2 * n_layers * n_heads * (input_length + output_length) * (dim // n_heads) * 2  # KV Cache（FP16）
        
        # 数据搬运时间
        data_movement_time = (param_size + kv_cache_size) / hbm_bandwidth
        
        # 计算时间
        compute_time = (prefill_ops + decode_ops) / peak_ops
        
        # 最终延迟（考虑硬件流水线并行）
        total_latency = max(compute_time, data_movement_time)
        
        return {
            'prefill_ops': prefill_ops,
            'decode_ops': decode_ops,
            'param_size_gb': param_size / 1e9,
            'kv_cache_size_gb': kv_cache_size / 1e9,
            'compute_time_ms': compute_time * 1000,
            'data_movement_time_ms': data_movement_time * 1000,
            'total_latency_ms': total_latency * 1000,
            'throughput_tokens_per_sec': output_length / total_latency
        }

# 模拟LLaMA-7B在BA-3000上的推理
runtime = BASARuntime(chip_count=1)
model_config = {
    'n_layers': 32,
    'dim': 4096,
    'n_heads': 32,
    'vocab_size': 32000,
    'ffn_dim': 4096 * 4
}

latency = runtime.estimate_latency(model_config)
print("=== BA-3000 LLaMA-7B推理延迟分析 ===")
for k, v in latency.items():
    if 'time' in k or 'latency' in k:
        print(f"{k}: {v:.2f}ms")
    elif 'gb' in k:
        print(f"{k}: {v:.2f} GB")
    else:
        print(f"{k}: {v:.2e}")

// BA-3000 vs Mobileye EyeQ6 自动驾驶推理性能对比
package main

import (
	"fmt"
	"math"
)

type PerfMetrics struct {
	name           string
	computeTOPS    float64
	powerW         float64
	efficiency     float64 // TOPS/W
	perceptionLatencyMs float64 // BEV感知延迟
	detectionAP    float64 // 检测精度mAP
	priceUSD       float64
}

func compareChips() {
	ba3000 := PerfMetrics{
		name:               "BA-3000",
		computeTOPS:        384,
		powerW:             30,
		efficiency:         12.8,
		perceptionLatencyMs: 28,
		detectionAP:        78.5,
		priceUSD:           299,
	}
	
	eyeQ6 := PerfMetrics{
		name:               "Mobileye EyeQ6",
		computeTOPS:        256,
		powerW:             45,
		efficiency:         5.7,
		perceptionLatencyMs: 35,
		detectionAP:        76.2,
		priceUSD:           450,
	}
	
	chips := []PerfMetrics{ba3000, eyeQ6}
	
	fmt.Println("=== XNGP芯片选型对比 ===\n")
	fmt.Printf("%-20s %12s %12s %12s %18s %12s %12s\n",
		"芯片", "算力(TOPS)", "功耗(W)", "能效(TOPS/W)", 
		"感知延迟(ms)", "检测精度(%)", "价格($)")
	fmt.Println("------------------------------------------------------------")
	
	for _, c := range chips {
		fmt.Printf("%-20s %12.0f %12.0f %12.1f %18.1f %12.1f %12.0f\n",
			c.name, c.computeTOPS, c.powerW, c.efficiency,
			c.perceptionLatencyMs, c.detectionAP, c.priceUSD)
	}
	
	// 计算系统级收益
	powerReduction := (eyeQ6.powerW - ba3000.powerW) / eyeQ6.powerW * 100
	latencyReduction := (eyeQ6.perceptionLatencyMs - ba3000.perceptionLatencyMs) / eyeQ6.perceptionLatencyMs * 100
	costReduction := (eyeQ6.priceUSD - ba3000.priceUSD) / eyeQ6.priceUSD * 100
	
	fmt.Printf("\n=== 系统级收益 ===\n")
	fmt.Printf("功耗降低: %.1f%%\n", powerReduction)
	fmt.Printf("延迟降低: %.1f%%\n", latencyReduction)
	fmt.Printf("成本降低: %.1f%%\n", costReduction)
	
	// 能效比优势
	fmt.Printf("\n=== 能效比分析 ===\n")
	fmt.Printf("BA-3000能效比: %.1f TOPS/W\n", ba3000.efficiency)
	fmt.Printf("EyeQ6能效比:   %.1f TOPS/W\n", eyeQ6.efficiency)
	fmt.Printf("BA-3000是EyeQ6的: %.1fx\n", ba3000.efficiency/eyeQ6.efficiency)
	
	// 每瓦推理能力
	baPerWatt := ba3000.computeTOPS / ba3000.powerW
	eyePerWatt := eyeQ6.computeTOPS / eyeQ6.powerW
	fmt.Printf("\n=== 每瓦推理能力 ===\n")
	fmt.Printf("BA-3000: %.1f TOPS/W\n", baPerWatt)
	fmt.Printf("EyeQ6:   %.1f TOPS/W\n", eyePerWatt)
	fmt.Printf("BA-3000优势: %.1fx\n", baPerWatt/eyePerWatt)
	
	// 自动驾驶场景FPS估算
	baFPS := 1000.0 / ba3000.perceptionLatencyMs
	eyeFPS := 1000.0 / eyeQ6.perceptionLatencyMs
	fmt.Printf("\n=== 实时感知帧率 ===\n")
	fmt.Printf("BA-3000: %.0f FPS\n", baFPS)
	fmt.Printf("EyeQ6:   %.0f FPS\n", eyeFPS)
	fmt.Printf("帧率提升: %.1f%%\n", (baFPS-eyeFPS)/eyeFPS*100)
}

func main() {
	compareChips()
}