Samsung Electronics plans to adopt its self-developed GPU in the Exynos 2800 chip by 2027, replacing the current Xclipse solution based on AMD RDNA. This move aims to reduce external dependencies and enhance technological autonomy. The GPU will initially be deployed in smartphones and gradually extended to smart glasses, robotics, automotive SoCs, and AI-specific chips, supporting the development of an end-to-end on-device AI ecosystem.
$Samsung Electronics Co., Ltd. (SSNLF.US)$ is advancing towards full autonomy in graphics processing units, planning to equip its Exynos chips, scheduled for release in 2027, with a self-developed GPU. This marks a critical step for the world's largest memory chip manufacturer in building an end-to-end AI ecosystem.
According to a report by The Korea Economic Daily on the 25th, Samsung aims to adopt self-developed graphics IP in the application processor Exynos 2800, which is scheduled for release in 2027, replacing existing collaborative solutions.
This move will reduce Samsung's reliance on external suppliers, granting it greater autonomy in functional and feature iterations. Samsung’s current mid-to-high-end mobile chips utilize the Xclipse GPU based on AMD's RDNA architecture.
Samsung plans to expand the application of its self-developed GPU from smartphones to markets including smart glasses, robotics, automotive SoCs, and AI-dedicated chips.
Reducing dependence on AMD, diversifying market presence
Samsung Electronics currently uses the Xclipse GPU, derived from the RDNA series architecture, in its mid-to-high-end mobile application processors. After transitioning to a fully self-developed graphics architecture, the company will gain greater control over product planning and technological roadmaps. $Advanced Micro Devices (AMD.US)$ After transitioning to a fully self-developed graphics architecture, the company will gain greater control over product planning and technological roadmaps.
The report states that Samsung plans to start with smartphones and gradually extend the use of its self-developed GPU to a broader range of devices, including smart glasses, robotics, and automotive chips, while entering the AI-dedicated chip market. This strategy aims to establish a complete edge AI product ecosystem.
Editor/Doris