Mixed-Precision Photonic Computing Platform

"""
Mixed-Precision Photonic Computing Implementation
Phase Change Material Enhanced Ring Resonators
"""

import numpy as np
from dataclasses import dataclass
from typing import Tuple, List, Dict

@dataclass
class PCMMaterial:
    """Phase Change Material properties"""
    name: str
    delta_n: float  # Refractive index change
    kc: float      # Extinction coefficient
    loss: float    # Loss in dB/cm
    levels: int    # Number of distinct levels

class PhotonicTensorCore:
    """
    Photonic Tensor Core with PCM-AlGaAs mem-resonators
    Implements mixed-precision in-memory computing
    """
    
    def __init__(self, size: int = 32, wavelengths: int = 32):
        self.size = size
        self.wavelengths = wavelengths
        
        # PCM Materials Database
        self.pcm_materials = {
            'Sb2S3': PCMMaterial('Sb₂S₃', 0.54, 0.05, 0.01, 32),
            'GST': PCMMaterial('Ge₂Sb₂Te₅', 2.74, 1.09, 1.0, 16),
            'Sb2Se3': PCMMaterial('Sb₂Se₃', 0.76, 0.0, 0.0, 32)
        }
        
        # System parameters
        self.msb_bits = 5  # PCM precision
        self.lsb_bits = 8  # Electro-optic precision
        self.total_bits = self.msb_bits + self.lsb_bits
        
        # Optical parameters
        self.wavelength_center = 1550e-9  # meters
        self.fsr = 200e9  # Free spectral range in Hz
        self.q_factor = 1.5e6
        
    def mixed_precision_iterative_refinement(
        self, A: np.ndarray, b: np.ndarray,
        low_bits: int = 6, tol: float = 1e-8
    ) -> Tuple[np.ndarray, int, List[float]]:
        """
        Mixed-precision iterative refinement algorithm
        Core algorithm for photonic in-memory computing
        """
        n = len(b)
        A_inv = np.linalg.inv(A)  # High precision, done once
        x = np.zeros(n)
        residuals = []
        
        for k in range(20):
            r = b - A @ x  # Compute residual
            residual_norm = np.linalg.norm(r)
            residuals.append(residual_norm)
            
            if residual_norm < tol:
                break
            
            # Analog correction (photonic computation)
            delta = self.analog_matvec(A_inv, r, bits=low_bits)
            x = x + delta
        
        return x, k + 1, residuals
    
    def analog_matvec(
        self, A: np.ndarray, x: np.ndarray, 
        bits: int, noise_std: float = 0.001
    ) -> np.ndarray:
        """Simulate photonic tensor core operation"""
        y = A @ x
        y_quant = self.quantize_to_bits(y, bits)
        
        if noise_std > 0:
            noise = np.random.normal(0, noise_std, y_quant.shape)
            y_quant += noise
        
        return y_quant
    
    def calculate_enob(self, power: float) -> float:
        """Calculate Effective Number of Bits"""
        power_dbm = 10 * np.log10(power * 1000)
        snr_db = power_dbm + 160 - 1.13  # RIN + insertion loss
        enob = (snr_db - 1.76) / 6.02
        return min(enob, self.total_bits)

# Example usage
core = PhotonicTensorCore(size=32, wavelengths=32)
results = core.benchmark_pde_solver(n_grid=64)

# Initialize the photonic computing system
from photonic_computing import PhotonicTensorCore, PhotonicSystemVisualizer

# Create tensor core with optimal configuration
core = PhotonicTensorCore(size=32, wavelengths=32)
core.set_pcm_material('Sb2S3')  # Low-loss material

# Optimize for 12-bit ENOB target
optimal = core.optimize_system_parameters(target_enob=12)
print(f"Optimal power: {optimal['power_watts']*1e3:.2f} mW")
print(f"Achieved ENOB: {optimal['achieved_enob']:.1f} bits")

# Benchmark PDE solver performance
results = core.benchmark_pde_solver(
    n_grid=64,
    precision_bits=[4, 6, 8, 10]
)

# Visualize convergence
viz = PhotonicSystemVisualizer()
viz.plot_convergence_analysis(results)
viz.plot_enob_analysis(core)

# Performance metrics
print(f"Peak Performance: {core.peak_performance/1e12:.2f} TeraOPS")
print(f"Energy Efficiency: {core.energy_efficiency:.2f} fJ/OP")