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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
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