OmniOS

AI-native, ultra-efficient and multi-sensory programmable OS for wearables

OmniOS

Tackling today’s challenge in ultra-low power wearables

The rise of ultra-low power wearables, designed to operate with firmware that is bare-bones and heavily constrained, poses significant challenges. These devices, often screenless, buttonless, or even battery-less, demand efficiency and intelligence far beyond the capabilities of traditional operating systems.

Current solutions fall short in supporting AI-native workloads, handling multi-sensory inputs, and enabling collaborative intelligence, limiting the potential of wearables in constrained environments. A new approach is urgently needed to meet these demands.

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Our solution
Key technology enablers
APA style publications
Project members
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Our solution

OmniOS is a groundbreaking AI-native operating system specifically designed for ultra-low power wearables operating under constrained conditions. With a focus on minimal firmware and optimal resource utilization, OmniOS redefines efficiency and functionality.

It leverages advanced technologies such as hetero-accelerator orchestration and data-centric collaborative AI to empower wearables to perform AI-native tasks seamlessly. This innovation enables devices to unlock new functionalities, ensuring they meet the demands of constrained environments while delivering exceptional performance and intelligence.

Key technology enablers

Hetero-accelerator orchestration

efficiently manages diverse computational resources to optimise AI workload execution for ultra-constrained devices

Data-centric collaborative AI

facilitates intelligent data-sharing across connected wearables, boosting performance and capabilities

Cross-service holistic storage management

enhances data handling across multiple devices, ensuring seamless operation in constrained environments

Software-defined thermal management

uses advanced software techniques to maintain optimal performance, even under stringent thermal limitations

OmniOS bridges the gap between constrained firmware environments and the increasing demands of AI-native applications, delivering unmatched efficiency and enabling new possibilities for next-generation wearables.

APA style publications

Gong, T., Kawsar, F., Min, C. "DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators" in Neural Information Processing Systems (NeurIPS), 2024
Min, C., Acer, U. G., Jang, S., Choi, S., Vasile, D. A., Gong, T., Yi, J., & Kawsar, F. (2024). An AI-Native Runtime for Multi-Wearable Environments. arXiv. https://arxiv.org/abs/2403.17863