HONG KONG, Sept. 18, 2026 (GLOBE NEWSWIRE) -- 3 E Network Technology Group Limited (NASDAQ:MASK) (the "Company" or "3 E Network"), a business-to-business ("B2B") information technology ("IT") business solutions provider, committed to becoming a next-generation artificial intelligence ("AI") infrastructure solutions provider, today officially outlined its foundational system architecture planned for Embodied AI and advanced robotics infrastructure. To advance the commercial implementation of this architecture, the Company also announced the completion of high-precision hardware emulation in a pre-silicon environment for its custom Edge AI SoC designed for Aladdin healthcare robots.

Optimizing Embodied AI Compute Architecture via the "Edge-Cloud Continuum"

Under the current evolution of the robotics industry, as the parameter sizes of large models continue to expand, the deployment of Embodied AI requires balancing among computing power requirements, power consumption limits, and manufacturing costs. An architecture relying entirely on local terminal computing is constrained by battery energy density and system thermal limits, making it difficult to simultaneously achieve high performance and cost efficiency for consumer applications. Conversely, purely cloud-based solutions must address household network latency fluctuations and global data privacy compliance standards.

3 E Network proposes that next-generation Embodied AI infrastructure should integrate traditional edge-cloud architectures. By constructing an "Edge-Cloud Continuum" that dynamically allocates computing and data-processing workloads, the system is designed to optimize resource allocation across edge and cloud environments:

Edge Compute Node (Real-Time Control and Data Isolation): Terminals deploy an Edge SoC based on heterogeneous computing architecture. This component focuses on processing time-sensitive commands—such as basic motion balance, 3D obstacle avoidance, and fall alerts—with deterministic microsecond-level latency. Concurrently, this node serves as a local data anonymization and pre-processing hub. It processes terabytes of raw multimodal data locally per hour, uploading only highly compressed abstract semantic instructions and critical corner case data. This design significantly reduces cloud transmission bandwidth requirements and helps keep highly sensitive visual and auditory data isolated at the hardware level locally.
Cloud Inference Platform (Complex Computing and Model Iteration): Resource-intensive tasks, including multimodal complex reasoning, long-term data analysis, and cross-robot federated learning, are offloaded to 3 E Network’s proprietary cloud AI SaaS platform. Through this coordinated allocation of computing workloads, 3 E Network aims to effectively reduce the hardware power consumption burden on individual terminals.

Optimizing System-Level Data Throughput with a Three-Tier AI Storage Architecture

Beyond compute allocation, the input/output (I/O) throughput capacity of multimodal data is another key challenge in Embodied AI system development. As computing power increases, the "Memory Wall" effect inherent in von Neumann architectures is becoming increasingly prominent. When terminals simultaneously activate high-resolution 3D spatial computing, multi-line LiDAR, and environmental array audio modules, the system generates large volumes of concurrent sensor data.

In the operational logic of advanced robotics, efficient data transmission is as critical as compute execution itself. To prevent compute core idling and system response delays caused by data transmission latency, 3 E Network has incorporated its "Full-Stack AI Storage Strategy" into its infrastructure architecture through an end-to-end three-tier data link:

Tier 1 (Edge Instantaneous Throughput): Utilizing high-bandwidth memory (HBM/LPDDR) tightly coupled with the Edge SoC, it provides high concurrent throughput for real-time sensor data and local model weights. This ensures millisecond-level synchronization between perception and control modules during emergency obstacle avoidance, reducing operational latency.
Tier 2 (Edge Local Caching): Building a local storage pool via high-speed NVMe protocols, it acts as an edge buffer to temporarily store high-frequency sensor data. This supports data-cleansing and feature-extraction algorithms, while functioning as the system’s operational data recorder.
Tier 3 (Cloud Concurrent Routing): Once anonymized and compressed semantic data is uploaded to the cloud, it is routed to 3 E Network’s enterprise-grade All-Flash Arrays. This architecture is designed to handle concurrent write requests from large-scale robot fleets, providing stable storage I/O support for cloud-based federated learning and continuous multimodal model iterations. By systematically integrating these three tiers via core data flow algorithms, 3 E Network aims to reduce data movement friction and ensure the efficient processing and movement of multimodal data throughout its lifecycle of "local generation, local processing," and "cloud routing."

Advancing System-Level Hardware Emulation Following Architectural Design

Following the announcement of the completed architectural design for the custom Edge SoC in July 2026, the 3 E Network team has advanced the project to the substantive validation phase. To advance the technical validation of the aforementioned underlying architecture, the Company recently conducted system-level testing using the semiconductor industry’s standard "Shift-Left" engineering methodology. The testing was completed within a matter of weeks. The R&D team utilized Virtual Prototyping for early software architecture exploration and combined it with high-performance Hardware Emulators to construct a cycle-accurate RTL logic mapping of the custom Edge SoC for Aladdin healthcare robots in a pre-silicon environment.

During Software-in-the-Loop (SIL) testing, the R&D team validated the low-power edge pre-processing workflow for visual and auditory data on the Edge SoC. This early-stage validation is designed to identify and mitigate potential bottom-layer logic risks prior to tape-out. It not only supports the mass production schedule for Aladdin robots but also establishes a solid hardware foundation for the upcoming, more complex "edge-cloud synergy" data link testing.

Management Commentary

Dr. Tingjun Yang, Chief Executive Officer of 3 E Network, summarized the Company’s infrastructure business: "The deployment of Embodied AI at scale in real-world applications requires stable and cost-effective underlying infrastructure to ensure the efficient utilization of computing power and communication networks. Our successful completion of custom Edge SoC emulation testing in a pre-silicon environment provides an engineering basis for validating this computing architecture. Future intelligent robotic terminals will require efficient computing resource allocation capabilities to enable collaborative operations between local processing and cloud resources.

As a neutral B2B compute and data infrastructure provider, we are committed to addressing system engineering challenges in the evolution of the robotics industry. 3 E Network’s long-term goal is to provide low-latency foundational data and compute services to global Embodied AI OEMs, research institutions, and R&D teams. By decoupling underlying chip architectures, AI storage orchestration and cloud SaaS and offering standardized services, we expect to substantially reduce the R&D barriers and commercialization costs for advanced robotics. The emulation data announced today demonstrates the engineering potential of our edge compute nodes. Moving forward, 3 E Network will continue to develop and refine this integrated hardware-software infrastructure to provide stable technical support for the scalable development of the Embodied AI industry."

About 3 E Network Technology Group Limited

3 E Network Technology Group Limited is a business-to-business ("B2B") information technology ("IT") business solutions provider committed to becoming a next-generation artificial intelligence ("AI") infrastructure solutions provider. It upholds the industry consensus of "AI and energy symbiosis" and has a strong vision in the field of energy investment. The Company’s business comprises two main portfolios: the data center operation services portfolio and the software development portfolio. For more information, please visit the Company’s website at https://3emask.com/.

Forward-Looking Statements

Certain statements in this announcement are forward-looking statements. These forward-looking statements involve known and unknown risks and uncertainties and are based on the Company’s current expectations and projections about future events that the Company believes may affect its financial condition, results of operations, business strategy, and financial needs. Investors can identify these forward-looking statements by words or phrases such as "approximates," "assesses," "believes," "hopes," "expects," "anticipates," "estimates," "projects," "intends," "plans," "will," "would," "should," "could," "may" or similar expressions. The Company undertakes no obligation to update or revise publicly any forward-looking statements to reflect subsequent events or circumstances, or changes in its expectations, except as may be required by law. Although the Company believes that the expectations expressed in these forward-looking statements are reasonable, it cannot assure you that such expectations will turn out to be correct, and the Company cautions investors that actual results may differ materially from the anticipated results and encourages investors to review other factors that may affect the Company’s future results in the Company’s registration statement and other filings with the U.S. Securities and Exchange Commission.

For more information, please contact:

3 E Network Technology Group Limited

Investor Relations Department

Email: ird@3emask.com

Website: https://3emask.com/