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NeuroSplit™

Build AI that adapts
to any device, anywhere.

NeuroSplitβ„’ 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.

60%
Cost Reduction
10x
Faster Response
5-10x
Larger Models

The Problem with Static AI Pipelines

Today's AI applications are built from static, hard-coded decisions that create brittle user experiences.

☁️

Hard-code to cloud GPU

Locked into high costs, latency, and privacy risks

πŸ“±

Hard-code to device

Limited model size, fails on weaker hardware

Static Pipeline (Current Approach)
Step 1
☁️ Cloud GPU
High Cost
β†’
Step 2
πŸ“± Device
Limited Models
β†’
Step 3
☁️ Cloud GPU
High Latency
❌ Fragile chain of static decisions

NeuroSplit™ Adaptive Solution

Real-Time Analysis

Network Latency 45ms
GPU Availability High
Device Capability Mid-Range
Privacy Policy Local Preferred
⚑

Adaptive Execution

Model Part 1
πŸ“± Local Device
65%
πŸ“Š Intermediate Result
Model Part 2
☁️ Cloud GPU
35%

Core Technology: Model Splitting

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.

🎯 The Optimal Split Problem

As networks grow, split possibilities grow exponentially. NeuroSplit's proprietary algorithm analyzes countless possibilities in real-time to find the optimal slice.

πŸ“Š The Unpredictable Device Problem

Consumer devices operate under constantly changing conditions. NeuroSplit uses ML to balance accuracy vs efficiency when measuring device state.

Neural Network Splitting
πŸ–₯️ Device Processing
Input Layer (784 neurons)
Hidden Layer 1 (512 neurons)
Hidden Layer 2 (256 neurons)
πŸ“Š Intermediate: 128-dim vector
☁️ Cloud Processing
Hidden Layer 3 (128 neurons)
Hidden Layer 4 (64 neurons)
Output Layer (10 classes)
βœ… Final Result

Simple Integration

Add NeuroSplit™ to your existing AI models with minimal code changes. The SDK handles all adaptive decision-making automatically.

πŸš€

Drop-in Replacement

Works with PyTorch, TensorFlow, and ONNX models

⚑

Real-time Adaptation

Automatically optimizes for each user's device and network

πŸ”’

Privacy-First

Keeps sensitive data on-device when possible

main.py
# 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)
app.js
// Traditional approach (static)
const model = await tf.loadLayersModel('/model.json');
const result = model.predict(inputData);

// NeuroSplit approach (adaptive)
import { NeuroSplit } from '@skymel/neurosplit';

const model = await tf.loadLayersModel('/model.json');
const adaptiveModel = await NeuroSplit.enable(model);

// Automatically adapts execution strategy
const result = await adaptiveModel.predict(inputData);

// Configuration options
const splitter = new NeuroSplit({
  privacyMode: 'strict',
  costOptimization: true,
  deviceProfile: 'auto-detect'
});
ModelManager.swift
// Traditional approach (static)
let model = try MLModel(contentsOf: modelURL)
let prediction = try model.prediction(from: input)

// NeuroSplit approach (adaptive)
import NeuroSplitSDK

let model = try MLModel(contentsOf: modelURL)
let adaptiveModel = try NeuroSplit.enable(model)

// Adapts based on iOS device capabilities
let prediction = try adaptiveModel.predict(input)

// Advanced configuration
let config = NeuroSplitConfig(
    privacyLevel: .high,
    batteryOptimized: true,
    networkAware: true
)
let splitter = NeuroSplit(config: config)

Brain + Nervous System Architecture

🧠

Skymel OA (The Brain)

Strategic planner that understands user goals and designs the perfect AI strategy

β€’ Analyzes user requirements
β€’ Creates executable task graphs
β€’ Plans optimal AI workflows
⚑
Task Graph
πŸ•ΈοΈ

NeuroSplitβ„’ (The Nervous System)

Tactical execution engine that brings OA's strategic plan to life with real-time adaptation

β€’ Distributes across device + cloud
β€’ Adapts to live conditions
β€’ Executes task graphs efficiently

Proven Performance Impact

πŸ’°
60%
Cost Reduction
Lower cloud compute costs by maximizing on-device processing
⚑
10x
Faster Response
Optimized execution paths and reduced network latency
🧠
5-10x
Larger Models
Deploy more capable AI while leveraging end-user devices
πŸ“Š
50-100
Stub Models
Fit multiple models in space of single quantized model
πŸ”’
Enhanced
Privacy
Process sensitive data locally whenever possible
πŸš€
Faster
Time-to-Market
Eliminate manual pipeline engineering and maintenance

Ready to Build Adaptive AI?

Join developers using NeuroSplitβ„’ to build more capable, cost-effective AI applications.

⚑ Quick Integration
πŸ”§ Developer-First
πŸ“š Full Documentation