This is a deep performance evaluation of the NPU of four chips from Rockchip: RK3588, RK3576, RK3568, and RK3562, integrating multiple practical data and technical analysis to compare their computing power, architecture, and application scenarios:
1.RK3588: Flagship-level AI computing power with full scene coverage
NPU Performance: Integrating the third-generation self-developed NPU, with a computing power of up to 6 TOPS, supporting INT4/INT8/INT16/FP16/BF16/TF32 mixed operations, capable of efficiently processing complex AI model inference.
Architecture and Compatibility: Supports TensorFlow, PyTorch, Caffe, MXNet, and other mainstream deep learning frameworks, enabling rapid model deployment through RKNN SDK. Supports multi-channel video stream parallel processing (e.g., 6 channels of MIPI input), suitable for 8K video real-time analysis and target tracking.
Measured Performance: In target detection (e.g., YOLOv5) and video stream processing, the 6 TOPS computing power can smoothly run high frame rate AI tasks, such as medical image analysis, vehicle surround view systems, and industrial automation scenarios.
Application Scenarios: Intelligent security, 8K multi-screen interaction, autonomous driving, medical devices, and other high-performance AIoT fields.
2. RK3576: AI performance on par with RK3588 NPU Performance: Shares the 6 TOPS computing power design with RK3588, also supporting various data precisions and mainstream AI frameworks, suitable for scenarios requiring high computing power but cost-sensitive.
Differentiated Features: Utilizes 8nm process technology, optimizing power consumption, suitable for edge computing devices. Its NPU supports model post-processing optimization (e.g., YOLO series model CPU acceleration), enhancing real-time performance.
Application Scenarios: Smart home, lightweight industrial AI devices, low-power edge servers, etc.
3. RK3568: A balanced choice for the mid-to-high-end market NPU Performance: Integrates 1 TOPS computing power NPU, supports RKNN toolchain, capable of running common AI tasks such as object detection and face recognition, but its computing power is only 1/6 that of RK3588.
Measured Performance: In multi-task video stream testing (e.g., three-screen display + AI inference), the CPU usage rate is about 30%, suitable for lightweight AI applications, but complex models require optimization or reduced precision processing.
Application Scenarios: Smart projectors, mid-range security cameras, industrial gateways, and other scenarios with less stringent computing power requirements.
4. RK3562: Entry-level AI capabilities NPU Performance: Specific computing power not explicitly mentioned, but based on positioning, its NPU performance is likely lower than that of RK3568, possibly focusing on basic image processing (e.g., JPEG hardware decoding) and simple AI inference.
Limitations: Supports only H.264 video stream processing, lacking high-resolution encoding/decoding capabilities, suitable for low-complexity tasks.
Application Scenarios: Low-power IoT devices, basic smart appliances, etc.
Comprehensive Comparison and Selection Recommendations
|
Model |
NPU Computing Power |
Process |
Typical Application Scenarios |
Advantages and Limitations |
|
RK3588 |
6 TOPS |
8nm |
High-end AIoT, 8K video processing |
Strong computing power, rich interfaces; relatively high cost |
|
RK3576 |
6 TOPS |
8nm |
Edge computing, lightweight AI |
Performance close to RK3588, power consumption optimized |
|
RK3568 |
1 TOPS |
22nm |
Mid-range smart devices, multi-screen interaction |
High cost-performance ratio, suitable for lightweight tasks |
|
RK3562 |
Not disclosed |
Not disclosed |
Basic IoT, simple image processing |
Low power consumption, limited functionality |
Summary of High Performance Requirements: Prioritize RK3588 or RK3576, whose 6 TOPS computing power can meet complex AI models and multi-modal data processing.
Mid-range scenarios:RK3568 with its 1 TOPS computing power and rich interfaces is suitable for projects with limited budgets but requiring basic AI functionalities.
Entry-level applications:RK3562 is suitable for scenarios with extremely low computing power requirements, emphasizing power control, but attention should be paid to its functional limitations.
If further technical details are needed (e.g., SDK usage or practical code), please refer to the corresponding development documentation and test cases provided by the manufacturer.