3 E Network Details Embodied AI Infrastructure and Completes Hardware Emulation for Custom Edge AI SoC
3 E Network Technology Group (MASK) detailed its planned Embodied AI and advanced robotics infrastructure and reported completing high-precision hardware emulation for its custom Edge AI SoC for Aladdin healthcare robots as of September 18, 2026.
Rhea-AI Summary
3 E Network Technology Group (MASK) detailed its planned Embodied AI and advanced robotics infrastructure and reported completing high-precision hardware emulation for its custom Edge AI SoC for Aladdin healthcare robots as of September 18, 2026.
The company proposes an “Edge-Cloud Continuum” that splits workloads between an edge compute node for real-time control and data anonymization, and a cloud inference platform for complex reasoning and federated learning. It also outlined a three-tier AI storage architecture spanning high-bandwidth memory, local NVMe caching, and cloud all-flash arrays to address I/O bottlenecks. Using virtual prototyping and hardware emulators, system-level, pre-silicon Software-in-the-Loop tests validated low-power visual and auditory pre-processing to de-risk tape-out and support Aladdin robot mass production plans.
Positive
- Custom Edge AI SoC emulation completed in a pre-silicon environment for Aladdin healthcare robots
- Edge-Cloud Continuum architecture defined, separating real-time edge control from cloud-based complex inference
- Three-tier AI storage design targets sensor data throughput via HBM/LPDDR, NVMe caching, and cloud all-flash arrays
- Software-in-the-Loop tests validated low-power visual and auditory pre-processing to reduce logic risks before tape-out
Negative
- None.
Details
Market Reaction – MASK
Following this news, MASK has gained 0.51%, reflecting a mild positive market reaction. Argus tracked a peak move of +4.8% during the session. Our momentum scanner has triggered 8 alerts so far, indicating moderate trading interest and price volatility. The stock is currently trading at $0.95. Trading volume is exceptionally heavy at 90.0x the average, suggesting very strong buying interest.
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Key Figures
- Edge-cloud architecture
- 3 tiers
- AI storage and data link architecture
- Edge processing latency
- microsecond-level
- Time-sensitive robotics commands
- Local data processing
- terabytes per hour
- Raw multimodal data at the edge
- Perception-control synchronization
- millisecond-level
- Emergency obstacle avoidance
Previous AI Reports
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Established customized AI storage-controller architecture and preliminarily validated core data-flow algorithms.
24h Move is the share-price change in the day after each event; other market factors may also have contributed.
Key Terms
embodied ai technical
pre-silicon technical
software-in-the-loop technical
nvme technical
AI-generated analysis. How Rhea-AI works. Not financial advice.
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/
FAQ
AI-generated questions and answers. How Rhea-AI works. Not financial advice.
What is the purpose of 3 E Network’s proposed “Edge-Cloud Continuum” for Embodied AI?
The “Edge-Cloud Continuum” is designed to dynamically allocate computing and data-processing workloads between edge and cloud. The edge node, built on a heterogeneous Edge SoC, handles time-sensitive tasks like motion balance, 3D obstacle avoidance, and fall alerts with microsecond-level latency and performs local anonymization of terabytes of multimodal data each hour. Compressed semantic instructions and corner cases are then sent to a cloud inference platform that performs complex reasoning, long-term analysis, and cross-robot federated learning, aiming to reduce terminal power consumption and bandwidth demands.
How does the three-tier AI storage architecture improve robotics data throughput?
The three-tier storage scheme couples high-bandwidth memory with the Edge SoC for instantaneous sensor and model data throughput, uses high-speed NVMe storage as an edge buffer and operational data recorder, and routes anonymized, compressed semantic data to enterprise all-flash arrays in the cloud. By coordinating these tiers with core data flow algorithms, the design seeks to limit data movement friction and maintain efficient multimodal data processing from local generation through cloud routing.
What engineering methods did 3 E Network use to validate the custom Edge SoC?
The R&D team applied a “Shift-Left” methodology, combining Virtual Prototyping with high-performance Hardware Emulators to build a cycle-accurate RTL mapping of the Edge SoC in a pre-silicon environment. During Software-in-the-Loop testing, they validated low-power edge pre-processing workflows for visual and auditory data. The company said this early validation is intended to identify and mitigate bottom-layer logic risks ahead of tape-out and to underpin later edge-cloud synergy data link testing.