[News] RISC-V Architecture in AI Chips Features “Three Advantages,” Meta’s in-house chip MTIA

In the global landscape of self-developed chips, the industry has predominantly embraced the Arm architecture for IC design. However, Meta’s decision to employ the RISC-V architecture in its self-developed AI chip has become a topic of widespread discussion. It is said the growing preference for RISC-V is attributed to three key advantages including low power consumption, high openness, and relatively lower development costs, according to reports from UDN News.

Noted that Meta exclusively deploys its in-house AI chip, “MTIA,” within its data centers to expedite AI computation and inference. In this highly tailored setting, this choice ensures not only robust computational capabilities but also the potential for low power consumption, with an anticipated power usage of under 25W per RISC-V core. By strategically combining the RISC-V architecture with GPU accelerators or Arm architecture, Meta aims to achieve an overall reduction in power consumption while boosting computing power simultaneously.

Meta’s confirmation of adopting RISC-V architecture form Andes Technology Corporation, a CPU IP and Platform IP supplier from Taiwan, for AI chip development underscores RISC-V’s capability to support high-speed computational tasks and its suitability for integration into advanced manufacturing processes. This move positions RISC-V architecture to potentially make significant inroads into the AI computing market,  and stands as the third computing architecture opportunity, joining the ranks of x86 and Arm architectures.

Regarding the development potential of different chip architectures in the AI chip market, TrendForce points out that in the current overall AI market, GPUs (such as NVIDIA, AMD, etc.) still dominate, followed by Arm architecture. This includes major data centers, with active investments from NVIDIA, CSPs, and others in the Arm architecture field. RISC, on the other hand, represents another niche market, targeting the open-source AI market or enterprise niche applications.
(Image: Meta)