#open-source ecosystem

Finloop Launches "RWA CONNECT 2026" to Build a Global Open-Source RWA Ecosystem | Digital More

HONG KONG, April 23, 2026 /PRNewswire/ -- At the "Connecting the World: Hong Kong RWA Infrastructure Ecosystem Seminar and RWA CONNECT 2026 Launch" held in

With the rapid development of edge computing and AIoT (Artificial Intelligence of Things), ARM-based SoCs have become the core of devices such as industrial controls, AI vision systems, and smart gateways. Currently, two highly representative processors are Broadcom's BCM2712 (used in the Raspberry Pi Compute Module 5, or CM5) and Rockchip's RK3588 (widely applied in high-performance ARM edge computers, such as the BL450 series). The former represents an ecosystem-driven general-purpose platform, while the latter emphasizes AI-driven high-performance computing. This article provides an in-depth comparison across five dimensions: architecture, performance, AI capabilities, ecosystem, and application directions, to help developers and engineers make informed selections.

CPU Architecture and Process Technology

The BCM2712 features a 4-core ARM Cortex-A76 architecture with a maximum clock speed of 2.4GHz and an approximately 16nm process node, making it a typical mid-to-high-performance embedded platform suitable for power-sensitive lightweight applications.

In contrast, the RK3588 employs an 8-core heterogeneous architecture (4×Cortex-A76 + 4×Cortex-A55) with a maximum clock speed of 2.4GHz, but advanced to an 8nm process. This provides superior power efficiency and computational performance.

In terms of raw computing power, the RK3588 outperforms the BCM2712 in multi-threaded tasks and heterogeneous processing, particularly in industrial AI applications requiring parallel tasks or background scheduling.

Graphics and Multimedia Performance

For GPU, the BCM2712 integrates the VideoCore VII graphics engine, supporting dual 4K video output, which is ideal for standard displays and lightweight graphics processing.

The RK3588 is equipped with the Mali-G610 MP4 GPU, enabling 8K video encoding/decoding, multi-channel display outputs, and advanced interfaces (e.g., HDMI 2.1, DP 1.4), excelling in video streaming and multi-screen applications.

As a result, in scenarios like video analytics, AI vision detection, and smart display terminals, the RK3588 demonstrates stronger image processing and display capabilities.

AI Computing Power and Edge Intelligence

AI performance shows the most significant gap between the two.

BCM2712: Lacks an integrated NPU, relying on CPU or GPU for AI inference, which is suitable for simple models but less efficient.

RK3588: Includes a built-in 6 TOPS NPU, supporting mainstream frameworks like TensorFlow, ONNX, and RKNN. It enables real-time on-device inference for neural networks such as YOLO, ResNet, and MobileNet.

In edge intelligence tasks like AI vision detection, object recognition, and speech processing, the RK3588 achieves millisecond-level local responses, significantly reducing latency and bandwidth usage without relying on cloud computing.

System Ecosystem and Software Support

The Raspberry Pi series is renowned for its vast open-source ecosystem, making the BCM2712 (CM5) platform highly developer-friendly:

Comprehensive official support for Raspberry Pi OS with strong compatibility;

Abundant reusable open-source projects, peripherals, and drivers;

Extensive documentation, tutorials, and community resources for quick onboarding.

By comparison, the RK3588's ecosystem leans toward industrial and professional development. Mainstream systems include Ubuntu, Buildroot, Yocto, and Debian, often provided as vendor-customized images for applications like industrial AI gateways and edge servers. While not as mature as Raspberry Pi's, it is optimized for high-load scenarios.

Application Directions and Selection Recommendations

The following table summarizes the advantages of each in typical application scenarios:Application ScenarioBCM2712 (CM5) BL460 Series Advantages RK3588 BL450 Series AdvantagesEducation & ResearchLow cost, rich ecosystem, easy developmentOverkill performance, poor cost-effectivenessIndustrial Data AcquisitionLow power consumption, long runtimeSupports more complex task processingAI Vision DetectionRequires external NPU or GPU reliance, moderate efficiencyBuilt-in 6 TOPS NPU, faster inferenceMultimedia DisplaySupports dual 4K screensSupports 8K multi-screen, high-definition decodingEdge ServersSuitable for lightweight edge computingIdeal for AIoT edge inference and video analytics

In summary, if the project prioritizes open-source ecosystem and rapid prototyping (e.g., educational development or lightweight industrial control), the Raspberry Pi CM5 BL460 series (BCM2712) is the top choice. For AI inference, video stream analysis, smart surveillance, or industrial automation requiring high computing power, ARM edge computers based on RK3588 (e.g., BL450 series) offer greater competitiveness.

Conclusion

The BCM2712 and RK3588 embody two distinct design philosophies: ecosystem-oriented and AI-performance-oriented. The former leverages Raspberry Pi's robust community for low barriers and high usability; the latter harnesses an advanced 8nm process and integrated NPU for efficient AI inference, ideal for industrial AI environments.

Looking ahead, as demand for AI edge computing continues to grow, the RK3588 will power more industrial and AIoT devices, while the Raspberry Pi CM5 maintains its unique strengths in education, R&D, and lightweight control domains.