3 E Network Finalizes Custom Edge AI SoC Architecture for Aladdin Robots, Delivering a Core Computing Foundation to Accelerate Commercialization

3 E Network Finalizes Custom Edge AI SoC Architecture for Aladdin Robots, Delivering a Core Computing Foundation to Accelerate Commercialization

July 20, 2026 07:50 ET | Source: 3E Network Technology Group Limited

HONG KONG, July 20, 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 announced a new development in its semiconductor business segment. Following the strategic framework agreement entered into with California-based advanced robotics enterprise Aladdin Alaris AI Inc. (“Aladdin Alaris AI”), the Company announced that it has completed key architectural design work and edge computing deployment planning for its custom Edge AI System-on-Chip (“SoC”), which is being designed for next-generation smart healthcare and eldercare robots.

Core Computational Challenges and Compliance Barriers of Eldercare Robots Compared to traditional industrial robotic arms physically isolated by “safety fences” in structured factories, the computational challenges facing smart eldercare robots represent a significant increase in complexity. They may operate without the protective barriers commonly used in industrial settings and must navigate dynamic, highly unstructured real-world home environments to engage in frequent, direct physical contact with humans, particularly older adults who may require additional care. This shift from isolated environments to high-frequency physical human-robot interaction imposes three strict technical requirements on the underlying compute power: High Real-Time Responsiveness and Physical Safety: When facing sudden emergencies like an elderly person falling, or when making physical contact to assist with standing, the robot must process massive amounts of multi-modal sensor data concurrently with ultra-low latency. Even hundreds of milliseconds of cloud network latency could result in serious safety hazards. Strict Compliance Requirements for Medical Privacy: To address applicable medical privacy requirements, including those that may arise under the U.S. Health Insurance Portability and Accountability Act of 1996 (“HIPAA”), high-definition video, audio footage, and vital sign data captured continuously in the home may require appropriate safeguards before being transmitted to or processed in the cloud. This mandates that the robot possess exceptionally robust “local closed-loop” computing capabilities. Network Blind Spots and Offline Reliability: Network blind spots and signal fluctuations frequently occur in complex and dynamic home environments. However, life-saving protection and natural emotional companionship cannot be contingent upon uninterrupted internet connectivity. In the event of a network outage, eldercare robots must possess a robust local computing foundation capable of independently executing advanced cognitive and caregiving strategies.

Faced with these complex, unstructured operating conditions, traditional merchant silicon prioritizing generalized benchmark metrics often falls short due to architectural limitations, suffering from latency, power, and bandwidth bottlenecks. This mismatch in underlying compute has contributed to the shift from general-purpose silicon to “scenario-defined” semiconductor design. Five Core Architectural Features of the Custom Edge AI SoC Consequently, 3 E Network’s custom-developed Edge AI SoC is focused on the optimization of edge inference capabilities. Utilizing a “hardware-software co-design” strategy, the chip architecture prioritizes low latency and energy efficiency for specific physical interactions, systematically addressing the underlying computing challenges of eldercare.