KDDI, Morgenrot and TOHKnet began a distributed data center demonstration utilizing retail robots in September 2026, with the aim of addressing power and GPU shortages in the AI era.
The Demonstration has been selected for the Ministry of Internal Affairs and Communications' "Watt-Bit Collaboration Demonstration Project".
In this Demonstration, KDDI's Osaka Sakai Data Center and Tama Network Center are connected through APN. In addition, a network that simulates connections between data centers and user sites are constructed within the Tama Network Center using a Point-to-Multipoint APN (Note 2). By combining GPUs installed at each center with the GPU virtualization platform developed and built by Morgenrot, distributed training and remote inference of physical AI using retail robots are conducted. Through this initiative, the optimal utilization method and effectiveness of computing resources distributed across multiple locations are verified.
KDDI promotes the "Digital Belt Initiative," under which assets such as data centers and submarine cables are integrated with technologies such as APN and 6G to build a nationwide low-latency network and AI computing infrastructure spanning land, sea, and air in an AI-centric society.
Going forward, the three companies will expand the APN connectivity area in this Demonstration to the Tohoku region and promote the creation of an environment that enables the effective utilization of computing resources and power available in regional areas. Furthermore, through the development of distributed data centers, they will accelerate the social implementation of Physical AI and help address labor shortages.
In recent years, with the growing adoption of AI, the social implementation of Physical AI has been advancing, particularly in industries such as retail and manufacturing. Because physical AI requires low-latency and stable processing, it is common to utilize computing infrastructure located in data centers and telecommunications facilities close to customer sites.
On the other hand, as the number of robots equipped with physical AI increases in the future, shortages of computing resources and power supply capacity at nearby data centers and telecommunications facilities are expected to become a challenge.
To address these challenges, achievement of "Watt-Bit Collaboration," which coordinates power supply and demand with telecommunications networks, is expected. To distribute physical AI processing to geographically remote data centers, low-latency and high-capacity communications are required. In addition, mechanisms are needed to flexibly switch processing destinations according to power supply-demand conditions and computing workloads while utilizing computing resources distributed across multiple sites as an integrated resource.
To achieve these mechanisms, APN providing low latency, high capacity, and low power consumption is important in ensuring the response performance required for physical AI even when remote data centers are utilized. Furthermore, technologies such as point-to-multipoint APN, which enables connections from a single site to multiple sites, and GPU virtualization platforms that allow integrated utilization of distributed GPU resources are also required.
Using a Point-to-Multipoint APN, network simulating connections between data centers and user sites are built within the Tama Network Center. The optical characteristics, traffic transmission performance, operational management functions, and power consumption of the Point-to-Multipoint APN are verified.
At a demonstration field within the Tama Network Center that simulates a retail store, training data related to robot operations are collected. The reduction in training time are evaluated for:
1. Training at a single nearby data center
2. Training at a single remote data center (Osaka Sakai Data Center)
3. Distributed training using both nearby and remote data centers
Remote inference processing for retail robot operation is comparatively evaluated under four GPU placement scenarios: embedded within the device (0 km), nearby within the prefecture (20 km or more), remote within the prefecture (50 km or more), and outside the prefecture (500 km or more).
