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AIO-3576Q |
AIO-3576JQ |
Basic Specifications |
SOC |
Rockchip RK3576
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Rockchip RK3576J
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CPU |
Octa-core 64-bit processor (4×A72 + 4×A53)
with a maximum frequency of 2.2GHz
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Octa-core 64-bit processor (4×A72 + 4×A53)
with a maximum frequency of 1.6GHz
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GPU |
G52 MC3 @ 1GHz, supports OpenGL ES 1.1/2.0/3.2, OpenCL 2.0, Vulkan 1.1,
embedded high-performance 2D acceleration hardware
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NPU |
6 TOPS computing power NPU, support INT4/INT8/INT16/FP16/BF16/TF32 operation, support dual-core collaborative
or independent work, support multi-task, multi-scenario parallelism
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ISP |
Built-in 16 million pixel ISP, support low-light noise reduction, support RGB-IR sensor,
support up to 120dB HDR, AI-ISP to improve low-noise image effect
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Codecs |
Decode: 8K@30fps/4K@120fps (H.265/HEVC, VP9, AVS2, AV1), 4K@60fps (H.264/AVC)
Encode:4K@60fps (H.265/HEVC, H.264/AVC)
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RAM |
LPDDR4/LPDDR4x (4GB/8GB/16GB Optional)
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Storage |
eMMC (16GB/32GB/64GB/128GB/256GB Optional), UFS2.0 (Optional)
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Storage
Expansion |
1 × M.2 M-KEY (Scalable SATA3.0/PCIe NVMe SSD, support 2242/2260/2280), 1 × TF Card
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Power |
DC 12V (5.5mm × 2.1mm, support 12V~24V wide voltage input)
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Power
consumption |
Max: 6W(12V/500mA), Normal: 1.8W(12V/150mA), Min: 0.624W(12V/52mA)
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OS |
It supports RTLinux kernel and has excellent real-time performance,
which is widely used in industrial application scenarios.
Support Android14, Linux OS, Buildroot, domestic operating system, provide a safe and stable system
environment for product research and production.
It has new industrial features such as real-time network, Flexbus, hardware resource isolation,
and DSMC to meet the needs of different industrial applications.
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AI
performance |
· Support the privatization deployment of ultra-large-scale parametric models under the Transformer architecture,
such as Gemma-2B, ChatGLM3-6B, Qwen-1.8B, Phi-3-3.8B and other large language models.
· It supports traditional network architectures such as CNN, RNN, and LSTM, and supports the import and
export of RKNN models; Support a variety of deep learning frameworks, including TensorFlow, TensorFlow Lite,
PyTorch, Caffe, ONNX and Darknet. It also supports the development of custom operators.
· Support Docker container management technology.
· It supports the real-time object detection algorithm YOLO (You Only Look Once),
which is fast and real-time compared with traditional object detection methods,
and can accurately identify and locate multiple target objects in images or videos, powering AI applications.
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Size |
145.9mm × 104.11mm × 26.38mm
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Weight |
≈149g
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Environment |
Operating Temperature: -20℃ ~ 60℃
Storage Humidity: 10%～90%RH (non-condensing)
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Operating Temperature: -40℃ ~ 85℃
Storage Humidity: 10%～90%RH (non-condensing)
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Interface Specifications |
Internet |
2 × Gigabit Ethernet (RJ45/1000Mbps), supports 2.4GHz/5GHz dual band WiFi (802.11a/b/g/n/ac),
Bluetooth 5.0, expandable 4G LTE (Mini PCIe), 5G (M.2 B-KEY)
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Video input |
2 × MIPI-CSI DPHY (1×4Lanes or 2×2Lanes, 30Pin-0.5mm)
1 × MIPI-CSI D/CPHY (DPHY(1×4 Lanes/2×2 Lanes) or CPHY(3Lanes), 24Pin-0.5mm)
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Video output |
1 × MIPI-DSI D/CPHY (30Pin-0.5mm, 2560×1600@60Hz)
1 × HDMI2.1 (4K@120fps)
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Audio |
1 × MIC (2Pin-1.25mm), 1 × 3.5mm Audio jack (Support MIC recording, American Standard CTIA)
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USB |
2 × USB3.0（HOST，Max1.2A）
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Button |
1 × MaskRom、1 × Reset、1 × Recovery
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Other
interfaces |
1 × SIM Card, 1 × Type-C (USB3.0 OTG, multiplexed with DP1.4), 1 × FAN (4Pin-1.25mm),
1 × Debug (3Pin-2mm), 1 × DC-IN (4Pin-2mm),
1 × Phoenix connector (2×4Pin-3.5mm): 1 × RS485, 1 × RS232, 1 × CAN 2.0
1 × Double-row pin headers (2×15-30Pin-2mm): 1×USB2.0, 1×SPI, 1×I2C, 1×UART, 3×A/D, 2×GPIO, Line-out
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