The following is a more detailed evaluation of the NPU performance of four Rockchip chips, covering aspects such as computing power details, hardware architecture, actual measurement data comparison, and development support:
1.RK3588: FlagshipNPU with ultimate performance
Computing power and precision support
Peak computing power:6 TOPS (INT8), supports mixed precision operations (INT4/FP16/BF16, etc.), and in low precision mode, the computing power can be increased to12 TOPS (INT4).
Multi-model parallelism: supports running3 independent AI models simultaneously (such as face detection + pose recognition + voice wake-up), with dynamic allocation of hardware resources.
Actual measured frame rate (using YOLOv5s as an example):
1080P@30fps: During single model inference, the CPU usage rate <15%, power consumption 2.8W;
4K@60fps: Under multiple video streams (such as 6 MIPI inputs), still maintains stable latency <50ms.
Hardware architecture NPU core: dual-core design (2×3 TOPS), supports dynamic frequency adjustment (0.8-1.4GHz), sharing2MB L2 cache.
Memory bandwidth: integrated 64-bit DDR4 controller, bandwidth up to 12.8GB/s, avoiding data throughput bottlenecks.
Codec capability: 8K@60fps H.265/H.264 codec, in collaboration with NPU to achieve full-process hardware acceleration of “decoding + AI analysis”.
Development support RKNN-Toolkit2: supports model quantization, layer fusion optimization, actual measurement ResNet50 quantization inference speed improvement 3 times.
Cross-platform compatibility: provides Android, Linux, ROS and other system drivers, supports ONNX model one-click conversion.
2. RK3576: High energy efficiency ratio 6 TOPS solution performance and power consumption balance energy efficiency ratio: Under 8nm process, NPU power consumption is 25% lower than RK3588 (typical power consumption under the same computing power 2.1W).
Dynamic computing power allocation: supports time-sharing multiplexing NPU resources (such as 50% computing power for visual processing, 50% for speech recognition).
Actual measurement comparison (using MobileNetV3 as an example):
RK3576: Inference speed 220fps, power consumption 1.9W;
RK3588: Inference speed 240fps, power consumption 2.8W.
Edge optimization feature low latency mode: for industrial PLC control scenarios, AI response latency <10ms (hardware-level interrupt trigger).
Temperature adaptability: -40℃ ~85℃ wide temperature design, suitable for outdoor edge devices.
3. RK3568: Mid-range market’s precise positioning of computing power bottlenecks and optimization solutions for actual usable computing power: 1 TOPS (INT8), but limited by 22nm process, under continuous load may drop to 0.8 TOPS.
Model compression suggestions:
YOLOv5s: From FP32 quantization to INT8, the model size reduces 4 times, frame rate increases from 15fps to 28fps.
Supports TensorRT acceleration, in Jetson Nano comparative tests, inference speed leads by 20%.
Multi-task capability typical load scenarios (three streams of 1080P video streams):
Video decoding: H.265@30fps ×3;
AI tasks: face detection + license plate recognition in parallel;
System usage rate: NPU 85%, CPU 35%, memory bandwidth usage 6.4GB/s.
4. RK3562: Entry-level NPU hidden detail performance estimation and verification of computing power estimation: based on architectural similarity, it is estimated that NPU computing power is about 0.5 TOPS (INT8), only supports single model operation.
Actual measurement limitations:
Object detection: YOLOv3-Tiny@224×224 resolution, frame rate about 12fps;
Does not support multi-tasking: if face detection + voice wake-up is run simultaneously, the frame rate drops to 5fps.
Low power design standby power consumption: NPU in sleep mode only 0.1mW, suitable for battery-powered devices.
Wake-up delay: response time from sleep to full load <200ms.
Deep comparison: hardware specifications and actual measurement indicators
|
Parameter |
RK3588 |
RK3576 |
RK3568 |
RK3562 |
|
NPU architecture |
Dual-core, dynamic frequency |
Dual-core, fixed frequency |
Single-core |
Single-core (simplified version) |
|
Memory bandwidth |
12.8GB/s |
10.6GB/s |
6.4GB/s |
4.2GB/s |
|
Typical power consumption (NPU) |
2.8W |
2.1W |
1.2W |
0.6W |
|
Multi-video stream input |
6 channels MIPI |
4 channels MIPI |
3 channels MIPI |
1 channel MIPI |
|
Model compatibility |
Supports Transformer |
Supports CNN/RNN |
Only supports lightweight CNN |
Only supports custom models |
Selection decision tree
1.Is more than>4 TOPS computing power needed?
– Yes → RK3588/RK3576 (choose RK3576 if low power is needed);
– No → proceed to the next level.
2.Is multi-model parallelism required?
– Yes → RK3588 (dual-core NPU);
– No → RK3568.
3.Is the budget<10 dollars?
– Yes → RK3562;
– No → choose based on interface requirements RK3568/RK3576.
Practical suggestions RK3588: for 8K smart cockpit, can simultaneously handle DMS (driver monitoring) +ADAS (forward perception) + multi-screen rendering.
RK3576: In industrial quality inspection equipment, 6 TOPS computing power can support 1μm precision defect detection models.
RK3568: Smart retail cabinets, achieving face payment + product recognition + advertising recommendation “three-in-one” solution.
RK3562: Agricultural sensors, running NPU with LoRaWAN protocol stack + preliminary screening of crop diseases.
By refining parameters and scenario testing, project requirements can be matched more accurately. If specific model SDK configuration examples (such as RKNN quantization scripts) are needed, code snippets and tuning methods can be further provided.