Functions and Characteristics
Functions and Characteristics
Opticalization of networks and computers
IOWN takes on the challenge of creating a world of well being sustainably. The key is the introduction of optical technologies. It is highly energy-efficient compared to electricity and is an important factor in achieving both improved performance and sustainability. As the transmission capacity (frequency) of data increases, electricity consumes a large amount of energy even over short distances (Figure 1). In recent years, as capacity increased, it became important to convert electricity into light, for example, when transferring data between neighboring computers or even within computers.
Figure 1 Low Power Consumption of Light
Simply replacing electricity with light will not achieve IOWN's lofty goals. It is necessary to review network and computer systems. Many points that can be improved in terms of energy efficiency in existing methods.
For example, in packet communication (TCP/IP protocol), which is currently the mainstream network method, data is always divided into packets, and the destination is added to each packet. Each point in the network (router) verifies the packet destination. This has to be switched back to electricity, even if you‘re using fiber for transmission. The IOWN All-Photonics Network (APN) rethinks this and completes all communications using optical communication without being switched back to electricity. By eliminating the need for data division and electrical conversion, ultra-low latency networks can be achieved.
Some improvements can be made when computers are processed in parallel or when data is transferred within a computer. For example, the CPU intervenes even between memory and accelerators such as GPUs. With the advent of AI, the use of many computers in parallel has increased, but the current CPU-centric approach is a very inefficient form of energy. IOWN's Data-Centric Infrastructure (DCI) redefines this approach to a data-centric approach. DCI can dramatically improve the power efficiency of your computer.
Overall optimization of different metrics
IOWN uses APN to transmit a far more variety and volume of information and process it through ICT to understand the current state of the world and society in more detail and predict the future. This will help to realize a world in which we can achieve well-being, but we are faced with the question of whether a single evaluation function can express the good and bad of this world.
We believe that conflicting values exist in this world and society in the form of competing values. We believe that it is important to accept this contradiction and embrace diversity and that this is an important point in building a world where we achieve well-being. We aim to achieve this by deriving solutions that are acceptable to each of the different indicators, even if they are not optimal for each of them.
Digital Twin Computing (DTC) is the key to achieving this. DTC is a technology that takes all the objects and phenomena in the world as a Digital Twin, predicts the future through various simulations, and seeks a harmonious solution, and psychology.
Observing, collecting, and using information on a large scale is no easy task. To do this sustainably, flexible control of ICT resources is also crucial. In this role is the Cognitive Foundation (CF), a set of functions that harmonize ICT resources holistically and optimally to distribute the necessary information. It is a technology that enables information to be handled in accordance with the location, application, environment, etc., without regard to the method or the operator.
Photonics-Electrics Convergence Devices and IOWN Roadmap
In the field of photonics-electrics convergence devices, we expect to see several leaps forward with this innovation. At IOWN1.0, we launched the All-Photonics Network (APN) service and achieved a delay target of 1/200 (Figure 2). The IOWN2.0 is a board-connected photoelectric device that advances the IOWN into the computer realm. It enables more flexible assembly of parts than ever before while achieving a highly energy-efficient computer. IOWN3.0 targets 125 times capacity and IOWN4.0 targets 100 times power efficiency. (Figures 2 and 3)
Figure 2 IOWN target performance
Figure 3 IOWN roadmap
