GigaDevice развивает стратегию на базе архитектуры RISC-V и внедряет искусственный интеллект в промышленную робототехнику

Компания GigaDevice обсуждает перспективы микроконтроллеров, делая акцент на гибкости архитектуры RISC-V и локальной обработке данных для роботов. Эксперты отмечают, что современные чипы Cortex-M уже способны выполнять задачи искусственного интеллекта на периферии, снижая зависимость от облачных вычислений.

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GigaDevice on RISC-V, Robotics and Edge AI

GigaDevice Explores RISC-V, Robotics and the Future of Microcontrollers Insights | 09-09-2026 | By Jack Pollard Microcontrollers have changed dramatically over the past two decades, not only in processing power and memory, but in the way engineers develop with them. In the latest Electropages Podcast, Robin Mitchell speaks with Kevin Jones, Business Development Manager at GigaDevice, about where MCU technology is heading next. One area they explore is the growing role of RISC-V. GigaDevice was an early mainstream adopter with the GD32VF103 in 2019, while its current wireless strategy also makes use of RISC-V-based devices. Kevin discusses the flexibility the architecture gives semiconductor manufacturers to develop processors around specific applications and workloads. Bringing More Intelligence Into Robotics Industrial robotics places a different set of demands on embedded hardware. As robots add more motors and increasingly sophisticated movement, communication between controllers needs to remain highly deterministic. Kevin discusses GigaDevice's GD32H7 devices with EtherCAT and how distributed microcontrollers can control individual motors and sections of a robotic system. Rather than sending every decision back to one central processor, future robots could increasingly make time-critical decisions locally. AI Moves Further Towards the Edge The conversation also turns to edge AI. Applications such as motor anomaly detection, human detection and other local inference tasks don't necessarily require a high-end AI processor. Kevin explains how Cortex-M7 and Cortex-M33 microcontrollers can already handle some of these workloads, particularly where models can be reduced to match the available processing resources. This could make local AI practical in more cost-sensitive embedded applications while reducing dependence on cloud processing. Watch the full Electropages Podcast below. Jack has spent over a decade in media within the electronics industry and is extremely passionate about working with companies to create interesting and educational content, from podcasts and video to written articles for engineers and buyers.