3 E Network Completes Hardware Emulation for Custom Edge AI SoC

HONG KONG, Sept. 18, 2026 /CourierPR/ -- 3 E Network Technology Group Limited, a business-to-business information technology solutions provider, has made significant strides in advancing its artificial intelligence infrastructure. The company has completed the high-precision hardware emulation for its custom Edge AI System on Chip (SoC) designed for its Aladdin healthcare robots, marking a critical milestone in its development.

According to Dr. Tingjun Yang, the Chief Executive Officer of 3 E Network, the company is committed to becoming a leading provider of next-generation artificial intelligence infrastructure. Yang emphasized, "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."

The Edge SoC, developed in July 2026, is a key component of 3 E Network's "Edge-Cloud Continuum" architecture. This architecture aims to optimize resource allocation between edge and cloud environments. The Edge SoC focuses on processing time-sensitive commands, such as basic motion balance, 3D obstacle avoidance, and fall alerts, with deterministic microsecond-level latency. It also serves as a local data anonymization and pre-processing hub, significantly reducing cloud transmission bandwidth requirements and keeping highly sensitive visual and auditory data isolated at the hardware level.

In addition to the Edge SoC, 3 E Network has also developed a three-tier AI storage architecture to optimize system-level data throughput. The architecture includes Tier 1, which utilizes high-bandwidth memory tightly coupled with the Edge SoC to provide high concurrent throughput for real-time sensor data and local model weights. Tier 2 acts as an edge buffer, temporarily storing high-frequency sensor data to support data-cleansing and feature-extraction algorithms. Tier 3, once anonymized and compressed semantic data is uploaded to the cloud, is routed to 3 E Network's enterprise-grade All-Flash Arrays, providing stable storage I/O support for cloud-based federated learning and continuous multimodal model iterations.

Dr. Yang further stated, "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."

By decoupling underlying chip architectures, AI storage orchestration, and cloud software as a service (SaaS), 3 E Network aims to substantially reduce the R&D barriers and commercialization costs for advanced robotics. The company's long-term goal is to provide low-latency foundational data and compute services to global Embodied AI original equipment manufacturers (OEMs), research institutions, and R&D teams.

The successful completion of the custom Edge SoC emulation testing in a pre-silicon environment demonstrates the engineering potential of 3 E Network's edge compute nodes. Moving forward, the company 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.