Add GR801 SoC radiation hardness simulation pipeline

simulation_pipeline_gr801.py

@@ -0,0 +1,264 @@
+# simulation_pipeline_gr801.py
+
+import logging
+from typing import Any
+
+import numpy as np
+
+LOGGER = logging.getLogger(__name__)
+
+
+# --- Data Structures ---
+class SoC:
+    """Model of the GR801 SoC."""
+    def __init__(self, num_cores: int, memory_size: int, accelerator_present: bool = True):
+        self.num_cores = num_cores
+        self.memory = np.zeros(memory_size, dtype=np.uint8)
+        self.accelerator_present = accelerator_present
+        self.registers = [0] * 32 * num_cores  # Assuming 32 registers per core
+        self.cache = np.zeros(1024, dtype=np.uint8)  # Simplified cache
+        self.errors = 0
+        self.performance = 0.0  # Some performance metric
+
+
+class RadiationModel:
+    """Models the radiation environment."""
+    def __init__(self, particle_flux: float, upset_rate: float):
+        self.particle_flux = particle_flux  # particles per cm^2 per second
+        self.upset_rate = upset_rate  # probability of an upset per particle
+
+
+class AIApplication:
+    """Represents an AI application running on the SoC."""
+    def __init__(self, task: str, input_data: np.ndarray):
+        self.task = task  # e.g., "image_classification"
+        self.input_data = input_data
+        self.output = None
+        self.accuracy = 1.0  # Current accuracy of the application
+
+
+class SimulationState:
+    """Holds the current state of the simulation."""
+    def __init__(self, soc: SoC, radiation: RadiationModel, app: AIApplication, time: float = 0.0):
+        self.soc = soc
+        self.radiation = radiation
+        self.app = app
+        self.time = time
+        self.faults_injected = 0
+        self.faults_corrected = 0
+
+
+# --- Initialization ---
+def initialize_soc(config: dict[str, Any]) -> SoC:
+    """Initialize the SoC with given configuration."""
+    num_cores = config.get('num_cores', 4)
+    memory_size = config.get('memory_size', 1024 * 1024)  # 1 MB
+    accelerator = config.get('accelerator', True)
+    return SoC(num_cores, memory_size, accelerator)
+
+
+def initialize_radiation_model(config: dict[str, Any]) -> RadiationModel:
+    """Initialize the radiation model."""
+    particle_flux = config.get('particle_flux', 1.0)  # particles/cm^2/s
+    upset_rate = config.get('upset_rate', 1e-5)  # upsets per particle
+    return RadiationModel(particle_flux, upset_rate)
+
+
+def initialize_ai_application(config: dict[str, Any]) -> AIApplication:
+    """Initialize the AI application."""
+    task = config.get('task', 'image_classification')
+    input_data = config.get('input_data', np.random.rand(100, 100))
+    return AIApplication(task, input_data)
+
+
+# --- Core Steps ---
+def run_ai_application(soc: SoC, app: AIApplication) -> None:
+    """Run the AI application on the SoC."""
+    # In a real simulation, this would involve running the neural network on the SoC.
+    # Here, we simulate by doing some computation and updating the application output.
+    if soc.accelerator_present:
+        # Use accelerator
+        # Simulate processing by doing a matrix multiplication (e.g., convolution)
+        # For simplicity, we'll just compute a dot product.
+        processed_data = np.dot(app.input_data.flatten(), app.input_data.flatten())
+    else:
+        # Use CPU cores
+        processed_data = np.sum(app.input_data)
+
+    # Store the result in memory (simplified)
+    soc.memory[0] = processed_data % 256
+    app.output = processed_data
+
+
+def inject_faults(soc: SoC, radiation: RadiationModel, dt: float) -> int:
+    """
+    Inject radiation-induced faults into the SoC.
+    Returns the number of faults injected.
+    """
+    # Calculate expected number of particles hitting the chip
+    chip_area = 1.0  # cm^2 (simplified)
+    expected_particles = radiation.particle_flux * chip_area * dt
+
+    # Poisson distribution for number of particles
+    num_particles = np.random.poisson(expected_particles)
+
+    # Each particle has a chance to cause an upset (bit flip)
+    faults = 0
+    for _ in range(num_particles):
+        if np.random.random() < radiation.upset_rate:
+            faults += 1
+            # Choose a random location to flip a bit
+            fault_type = np.random.choice(['memory', 'register', 'cache'])
+            if fault_type == 'memory':
+                address = np.random.randint(0, len(soc.memory))
+                bit = np.random.randint(0, 8)
+                soc.memory[address] ^= (1 << bit)
+            elif fault_type == 'register':
+                reg = np.random.randint(0, len(soc.registers))
+                soc.registers[reg] ^= 1
+            else:  # cache
+                address = np.random.randint(0, len(soc.cache))
+                bit = np.random.randint(0, 8)
+                soc.cache[address] ^= (1 << bit)
+
+    soc.errors += faults
+    return faults
+
+
+def apply_fault_tolerance(soc: SoC, correction_rate: float = 0.8) -> int:
+    """
+    Apply fault tolerance mechanisms to correct errors.
+    Returns the number of faults corrected.
+
+    Args:
+        soc: The SoC instance to apply fault tolerance to
+        correction_rate: Fraction of errors that can be corrected (default: 0.8)
+    """
+    # Simplified: Assume we can correct some errors with ECC in memory and cache.
+    corrected = int(soc.errors * correction_rate)
+    soc.errors -= corrected
+    return corrected
+
+
+def update_radiation_model(radiation: RadiationModel, dt: float) -> None:
+    """Update the radiation model over time (e.g., change flux)."""
+    # For simplicity, we keep the radiation model constant.
+    # In a real simulation, we might change it based on orbit, solar activity, etc.
+    pass
+
+
+def monitor_state(state: SimulationState) -> dict[str, Any]:
+    """Monitor the simulation state and collect metrics."""
+    metrics = {
+        'time': state.time,
+        'errors': state.soc.errors,
+        'performance': state.soc.performance,
+        'total_faults_injected': state.faults_injected,
+        'total_faults_corrected': state.faults_corrected,
+        'application_accuracy': state.app.accuracy,
+    }
+    return metrics
+
+
+def log_state(metrics: dict[str, Any]) -> None:
+    """Log the current state."""
+    LOGGER.info(
+        "Time: %.2fs, Errors: %d, Performance: %.2f",
+        metrics['time'], metrics['errors'], metrics['performance']
+    )
+
+
+def safety_violation_detected(state: SimulationState, error_threshold: int = 1000) -> bool:
+    """Check for safety violations (e.g., too many errors).
+
+    Args:
+        state: Current simulation state
+        error_threshold: Maximum allowed errors before triggering shutdown (default: 1000)
+    """
+    if state.soc.errors > error_threshold:
+        LOGGER.warning("Safety violation: Too many errors (%d)", state.soc.errors)
+        return True
+    return False
+
+
+def trigger_safe_shutdown(state: SimulationState) -> None:
+    """Trigger a safe shutdown of the system."""
+    LOGGER.warning("Triggering safe shutdown")
+    # Save critical data, power down, etc.
+
+
+# --- Main Loop ---
+def run_simulation(time_steps: int, dt: float, config: dict[str, Any]) -> list[dict[str, Any]]:
+    """
+    Run the simulation for a given number of time steps.
+    Returns a list of state metrics for each time step.
+
+    Config parameters:
+        - correction_rate: Fraction of errors corrected each step (default: 0.8)
+        - error_threshold: Max errors before shutdown (default: 1000)
+    """
+    # Initialize
+    soc = initialize_soc(config)
+    radiation = initialize_radiation_model(config)
+    app = initialize_ai_application(config)
+
+    # Get optional config parameters
+    correction_rate = config.get('correction_rate', 0.8)
+    error_threshold = config.get('error_threshold', 1000)
+
+    state = SimulationState(soc, radiation, app, time=0.0)
+
+    metrics_history = []
+
+    for t in range(time_steps):
+        # Run the AI application
+        run_ai_application(soc, app)
+
+        # Inject faults due to radiation
+        faults = inject_faults(soc, radiation, dt)
+        state.faults_injected += faults
+
+        # Apply fault tolerance
+        corrected = apply_fault_tolerance(soc, correction_rate)
+        state.faults_corrected += corrected
+
+        # Update performance metric after fault handling
+        soc.performance = 1.0 / (1.0 + soc.errors)  # Simplified: errors reduce performance
+
+        # Update radiation model (if dynamic)
+        update_radiation_model(radiation, dt)
+
+        # Update time
+        state.time += dt
+
+        # Monitor and log
+        metrics = monitor_state(state)
+        metrics_history.append(metrics)
+
+        if t % 10 == 0:
+            log_state(metrics)
+
+        # Check for safety violations
+        if safety_violation_detected(state, error_threshold):
+            trigger_safe_shutdown(state)
+            break
+
+    return metrics_history
+
+
+# --- Example Configuration and Run ---
+if __name__ == "__main__":
+    config = {
+        'num_cores': 4,
+        'memory_size': 1024 * 1024,
+        'accelerator': True,
+        'particle_flux': 5.0,  # High radiation environment
+        'upset_rate': 1e-4,
+        'task': 'image_classification',
+        'input_data': np.random.rand(100, 100),
+    }
+
+    dt = 0.1  # 0.1 second per time step
+    time_steps = 100
+
+    history = run_simulation(time_steps, dt, config)