src/hardware/neural_processing_unit.py

from dataclasses import dataclass
from typing import Dict, List, Optional
import numpy as np
import torch

@dataclass
class NPUState:
    load_level: float
    active_cores: int
    memory_usage: Dict[str, float]
    temperature: float
    processing_efficiency: float

class SparseActivationManager:
    def compute_pattern(self, input_data: torch.Tensor) -> torch.Tensor:
        # Placeholder implementation
        return input_data

class ExpertRoutingSystem:
    def allocate_experts(self, activation_pattern: torch.Tensor) -> Dict[str, int]:
        # Placeholder implementation
        return {"expert1": 1, "expert2": 2}

class NeuralProcessingUnit:
    def __init__(self, num_cores: int = 128):
        self.num_cores = num_cores
        self.state = NPUState(
            load_level=0.0,
            active_cores=0,
            memory_usage={},
            temperature=0.0,
            processing_efficiency=1.0
        )
        self.sparse_activation = SparseActivationManager()
        self.expert_router = ExpertRoutingSystem()

    async def process_neural_task(self, input_data: torch.Tensor) -> torch.Tensor:
        activation_pattern = self.sparse_activation.compute_pattern(input_data)
        expert_allocation = self.expert_router.allocate_experts(activation_pattern)
        return await self._execute_neural_computation(input_data, expert_allocation)

    async def _execute_neural_computation(self, input_data: torch.Tensor, expert_allocation: Dict[str, int]) -> torch.Tensor:
        # Placeholder implementation
        return input_data