FUS-Meta is an innovative offline-first AI and optimization framework designed to empower edge devices with self-adaptive AI capabilities. This framework enables complex AI training and optimization processes to be executed directly on hardware such as MCU, FPGA, and ASIC, ensuring that operations are completely cloud-free and enhancing user privacy.

Key Features

• Adaptive Neural Architecture Design: Automatically identifies optimal neural network structures tailored for various hardware constraints, significantly minimizing manual tuning time by 30-70%.
• Performance Enhancement: Achieves an improvement in accuracy of 2-5% on noisy industrial datasets while simultaneously optimizing for power, latency, and memory.
• Full Offline Operation: Designed for secure industrial applications, FUS-Meta runs entirely offline, safeguarding sensitive data from potential leaks.

Philosophy

"If it can run on a phone, it can run anywhere—privately."

Projects within the FUS-Meta Ecosystem

The FUS-Meta ecosystem includes multiple projects aimed at expanding the capabilities of edge AI:

• FUS-Meta AutoML: A public beta no-code AutoML tool for Android that allows users to upload CSV files and receive a trained PyTorch model—all offline.
• FUS-Meta Optimizer: A stable core offering a quantum-inspired adaptive optimizer for larger-scale problems such as MAX-CUT and QUBO.
• Edge AI Suite: A research-focused project providing ultra-lightweight, self-adaptive neural networks tailored for microcontrollers and edge applications.
• GravOpt-MAXCUT: A production-ready high-performance heuristic designed to tackle MAX-CUT problems for very large graphs, with high efficiency on CPU.

Use Cases

FUS-Meta is suitable for various industries and applications:

• Healthcare: Enables training on sensitive patient data while maintaining network privacy, along with real-time analysis for medical IoT devices.
• Industrial Optimization: Facilitates on-premise solutions for logistical challenges, scheduling, and resource allocation.
• Privacy-First AI: Performs high-stakes analysis on financial and personal data without compromising data security.
• Education & Prototyping: Provides students with the opportunity to work with advanced AI and optimization tools without cloud dependencies.

Performance Highlights

• MAX-CUT (50k nodes) on Laptop CPU (i7): Achieved 99.17% optimal in approximately 9 seconds.
• AutoML on Iris dataset using Android Phone + Local Docker: Reached 97% accuracy in under 10 seconds.
• Edge Inference on ESP32: Demonstrated latency of less than 100ms and memory usage below 256KB.

FUS-Meta is currently in active beta, welcoming contributions from users interested in testing the AutoML tool, benchmarking performance across different hardware, or proposing use cases from healthcare, industrial sectors, or research.

For those interested in joining the FUS-Meta community, visit the Telegram group for beta testers and developers, where support, bug reporting, and discussions on edge AI advancements take place.

With FUS-Meta, experience the future of AI where performance, privacy, and accessibility converge seamlessly on edge devices.