A unified compute and control layer powering all AVA optimization modules and node-level intelligence

The AVA Core is the central software engine of the entire Resonant Intelligence architecture. It coordinates every component of the system — from the optimization modules that enhance model execution, to the hardware-level AVA Nodes, all the way to the RI-Net distributed network.

Its purpose is to create a unified, stable, and energy-efficient operational environment in which all AVA modules collaborate, continuously self-optimize, and intelligently adapt to workload changes and system conditions.

What does AVA Core do?

The AVA Core consists of three primary functional layers:

1. Operational and Control Layer

• – orchestrates all optimization modules (RCF-LIM, HGO, ADC-Optim, RCF-Secure)
• – monitors system behavior in real time
• – determines optimal execution strategies dynamically
• – ensures stable operation under varying workloads

2. Resonant Pattern Analysis Layer

• – identifies internal behavioral patterns across models and system processes
• – optimizes compute flows and energy distribution
• – reduces redundant operations
• – tunes system “resonance” to match the current task

3. Network Coordination Layer

• – connects AVA Nodes into the RI-Net structure
• – manages workload distribution across nodes
• – ensures synchronization and fault tolerance
• – applies energy-aware communication strategies

Key advantages

• – central unified engine for the entire AVA ecosystem
• – delivers at least 30–40% overall efficiency improvements
• – ensures consistent, predictable performance
• – provides real-time adaptivity and intelligent scaling
• – significantly reduces compute and energy waste
• – high data security and modular expansion capability
• – deployable in cloud, on-premise and edge environments

Where can it be deployed?

• – enterprise AI infrastructure as the central orchestration layer
• – AVA Node clusters running in RI-Net
• – national or sector-level AI platforms (education, healthcare, industry, government)
• – research and university networks
• – local edge-AI deployments requiring autonomous operation

Technology foundations

The AVA Core:

• – executes and coordinates the Resonant Optimization Suite
• – integrates Meta-Hopfield structures for predictive pattern modeling
• – manages GPU–CPU balancing through HGO
• – oversees energy distribution via ADC-Optim
• – provides security and anomaly detection through RCF-Secure
• – connects directly into the RI-Net distributed intelligence layer

Project status

• – ready-to-start project, with complete architectural foundation
• – compatible with enterprise and edge infrastructures
• – suitable for rapid pilot implementation
• – low integration risk
• – core component of the overall AVA Intelligence System