サクラ ラボズ
Sakura Labs
[sah-koo-rah labz]: proper noun
where innovation blooms like cherry blossoms.
Decentralized AI Infrastructure
Technical Overview of Sakura Labs' Core Systems
Our distributed AI system utilizes a novel approach to consensus through what we call "Neural Sharding" - a process that parallelizes AI computations across the network while maintaining data consistency.
class NeuralShard:
def __init__(self, shard_id: int, capacity: int):
self.shard_id = shard_id
self.capacity = capacity
self.neural_cache = LRUCache(capacity)
async def process_computation(self, input_data: Tensor) -> Tensor:
if self.neural_cache.contains(input_data.hash()):
return self.neural_cache.get(input_data.hash())
result = await self.distributed_inference(input_data)
self.neural_cache.put(input_data.hash(), result)
return resultOur modified Proof of Neural Stake (PoNS) algorithm achieves consensus through:
Where:
Wi = Validator weight
Vi = Validation accuracy
Si = Stake amount
pub struct NeuralValidator {
pub stake: u64,
pub accuracy_history: Vec,
pub weight: f64,
}
impl NeuralValidator {
pub fn calculate_consensus_power(&self) -> f64 {
let avg_accuracy = self.accuracy_history.iter().sum::() /
self.accuracy_history.len() as f64;
(self.weight * avg_accuracy) * (self.stake as f64 + 1.0).ln()
}
} Token distribution follows a logarithmic decay function to ensure long-term sustainability:
Where:
R₀ = Initial reward rate
λ = Decay constant
α = Minimum reward floor