NeuroSplit™ is an adaptive hybrid inference engine that intelligently slices and distributes AI models across devices and cloud at runtime. No more brittle, static AI pipelines.
Today's AI applications are built from static, hard-coded decisions that create brittle user experiences.
NeuroSplit™ can slice an AI model's neural network connections in real-time, creating two models from one where the output of the first feeds as input to the second.
As networks grow, split possibilities grow exponentially. NeuroSplit's proprietary algorithm analyzes countless possibilities in real-time to find the optimal slice.
Consumer devices operate under constantly changing conditions. NeuroSplit uses ML to balance accuracy vs efficiency when measuring device state.
Add NeuroSplit™ to your existing AI models with minimal code changes. The SDK handles all adaptive decision-making automatically.
Works with PyTorch, TensorFlow, and ONNX models
Automatically optimizes for each user's device and network
Keeps sensitive data on-device when possible
# Traditional approach (static)
import torch
model = torch.load('my_model.pth')
result = model(input_data)
# NeuroSplit approach (adaptive)
import neurosplit
model = torch.load('my_model.pth')
adaptive_model = neurosplit.enable(model)
# Now automatically adapts to device/network conditions
result = adaptive_model(input_data)
# Advanced: Custom splitting strategies
splitter = neurosplit.Splitter(
privacy_level='high', # Prefer local processing
cost_optimization=True, # Minimize cloud costs
latency_target=100 # Target 100ms response
)
adaptive_model = splitter.wrap(model)Strategic planner that understands user goals and designs the perfect AI strategy
Tactical execution engine that brings ADK's strategic plan to life with real-time adaptation
Join developers using NeuroSplit™ to build more capable, cost-effective AI applications.