| Feature | RK3568 Industrial Edge Specs |
|---|---|
| Processor | Rockchip RK3568 (4× Cortex-A55 @ 2.0GHz) |
| Acceleration | Arm Mali-G52 GPU + 0.8 TOPS NPU |
| Memory/Storage | 4GB LPDDR4X + 32GB eMMC 5.1 (expandable via M.2 2280 SATA) |
| OS | Android 12 GMS Certified (AOSP 10-year support) |
| Operating Temp | -40°C to +85°C(conformal coating) |
| Power Input | 9-36V DC (ISO 7637-2 compliant) |
| Ingress Protection | IP65 Aluminum Chassis(fanless design) |
| Wireless | Dual-band Wi-Fi 6, BT 5.2, 4G/LTE (Quectel EG25-G) |
| Security | TEE (Trustonic Kinibi), Secure Boot, OTA with Uptane |
| Interface | Industrial Use Case |
|---|---|
| GPIO | 16× digital I/O (optically isolated, 2500Vrms) + 4× ADC (16-bit) |
| Fieldbus | 2× CAN 2.0B (isolated), RS-232/485 (ADM3251E) |
| Networking | Dual GbE w/ PoE+ (802.3at)support |
| Camera | 2× MIPI-CSI (4-lane) for machine vision |
| Expansion | Mini-PCIe slot (for Zigbee 3.0, LoRaWAN, etc.) |
Edge AI Capabilities
NPU Acceleration: Runs TensorFlow Lite models for defect detection (≤50ms inference)
Vision Pipeline: 4K@30fps H.265 decode + ISP (HDR, 3A algorithms)
Extreme Environment Resilience
Vibration: 5Grms (IEC 60068-2-64)
EMC: EN 55032/35 Class A, ±8kV ESD protection
Chemical Resistance: Conformal coating per IPC-CC-830B
Power Management
Ignition Surge Protection: Handles 40V load dump (ISO 7637-2 Pulse 5)
Supercapacitor Backup: 5-min runtime for graceful shutdown
| Workload | RK3568 Performance |
|---|---|
| Barcode Scanning | 60 fps (ZBar + OpenCV on NPU) |
| Modbus RTU Polling | 100 devices @ 100ms cycle |
| Inference Latency | MobileNetV2 SSD: 48ms (NPU) |
| Power Consumption | 3W (idle) / 8W (peak) |
GPIO Hardening
Opto-Isolation: Use Vishay VO615A for input channels
Relay Drivers: Infineon BTS50085 for >2A loads
Thermal Management
Arctic Ops: Apply Bergquist Hi-Flow 300thermal pads below -20°C
High-Temp Derating: Throttle CPU at >75°C ambient
Network Reliability
PoE Splitters: STSPIN32F0 for 24V output to sensors
CAN Bus Termination: 120Ω resistors with TVS diodes (SM24CANA)
Smart Factory: Machine vision QC + OPC UA data aggregation
AGV Control: ROS 2 navigation + CAN-based motor control
Renewable Energy: Solar inverter monitoring via Modbus TCP
Fleet Telematics: J1939/CAN FD logging + 4G cloud sync
| Platform | Advantage of RK3568 Industrial |
|---|---|
| Raspberry Pi CM4 | Wider temp range, isolated I/O, 9-36V input |
| Jetson Nano | Lower power, Android support, 5G ready |
| Advantech ARK | 50% cost savings, equivalent GPIO density |
Bottom Line: This RK3568-based edge computer delivers industrial-grade Android in a fanless, wide-temperature package – ideal for consolidating PLCs, vision systems, and IIoT gateways. Validate IEC 60068-2-27 shock reports and demand -40°C cold-start demos.
Recommended Accessories:
DIN Rail Kit: Phoenix Contact URD 35
PoE Splitter: STSPIN32F0 (24V/3A output)
Industrial Antenna: Taoglas MA.57.A (4G/LTE)
GPIO Breakout: Wago 789-104 (spring-clamp terminals)
Outdoor Enclosure: Rittal SK 3321 (IP66, -40°C to 75°C)
For AI deployments:
Use TensorFlow Lite Delegates for NPU acceleration
Enable MIPI-CSI HDR mode for high-contrast environments
Integrate Azure Percept for cloud-model orchestration
| Feature | RK3568 Industrial Edge Specs |
|---|---|
| Processor | Rockchip RK3568 (4× Cortex-A55 @ 2.0GHz) |
| Acceleration | Arm Mali-G52 GPU + 0.8 TOPS NPU |
| Memory/Storage | 4GB LPDDR4X + 32GB eMMC 5.1 (expandable via M.2 2280 SATA) |
| OS | Android 12 GMS Certified (AOSP 10-year support) |
| Operating Temp | -40°C to +85°C(conformal coating) |
| Power Input | 9-36V DC (ISO 7637-2 compliant) |
| Ingress Protection | IP65 Aluminum Chassis(fanless design) |
| Wireless | Dual-band Wi-Fi 6, BT 5.2, 4G/LTE (Quectel EG25-G) |
| Security | TEE (Trustonic Kinibi), Secure Boot, OTA with Uptane |
| Interface | Industrial Use Case |
|---|---|
| GPIO | 16× digital I/O (optically isolated, 2500Vrms) + 4× ADC (16-bit) |
| Fieldbus | 2× CAN 2.0B (isolated), RS-232/485 (ADM3251E) |
| Networking | Dual GbE w/ PoE+ (802.3at)support |
| Camera | 2× MIPI-CSI (4-lane) for machine vision |
| Expansion | Mini-PCIe slot (for Zigbee 3.0, LoRaWAN, etc.) |
Edge AI Capabilities
NPU Acceleration: Runs TensorFlow Lite models for defect detection (≤50ms inference)
Vision Pipeline: 4K@30fps H.265 decode + ISP (HDR, 3A algorithms)
Extreme Environment Resilience
Vibration: 5Grms (IEC 60068-2-64)
EMC: EN 55032/35 Class A, ±8kV ESD protection
Chemical Resistance: Conformal coating per IPC-CC-830B
Power Management
Ignition Surge Protection: Handles 40V load dump (ISO 7637-2 Pulse 5)
Supercapacitor Backup: 5-min runtime for graceful shutdown
| Workload | RK3568 Performance |
|---|---|
| Barcode Scanning | 60 fps (ZBar + OpenCV on NPU) |
| Modbus RTU Polling | 100 devices @ 100ms cycle |
| Inference Latency | MobileNetV2 SSD: 48ms (NPU) |
| Power Consumption | 3W (idle) / 8W (peak) |
GPIO Hardening
Opto-Isolation: Use Vishay VO615A for input channels
Relay Drivers: Infineon BTS50085 for >2A loads
Thermal Management
Arctic Ops: Apply Bergquist Hi-Flow 300thermal pads below -20°C
High-Temp Derating: Throttle CPU at >75°C ambient
Network Reliability
PoE Splitters: STSPIN32F0 for 24V output to sensors
CAN Bus Termination: 120Ω resistors with TVS diodes (SM24CANA)
Smart Factory: Machine vision QC + OPC UA data aggregation
AGV Control: ROS 2 navigation + CAN-based motor control
Renewable Energy: Solar inverter monitoring via Modbus TCP
Fleet Telematics: J1939/CAN FD logging + 4G cloud sync
| Platform | Advantage of RK3568 Industrial |
|---|---|
| Raspberry Pi CM4 | Wider temp range, isolated I/O, 9-36V input |
| Jetson Nano | Lower power, Android support, 5G ready |
| Advantech ARK | 50% cost savings, equivalent GPIO density |
Bottom Line: This RK3568-based edge computer delivers industrial-grade Android in a fanless, wide-temperature package – ideal for consolidating PLCs, vision systems, and IIoT gateways. Validate IEC 60068-2-27 shock reports and demand -40°C cold-start demos.
Recommended Accessories:
DIN Rail Kit: Phoenix Contact URD 35
PoE Splitter: STSPIN32F0 (24V/3A output)
Industrial Antenna: Taoglas MA.57.A (4G/LTE)
GPIO Breakout: Wago 789-104 (spring-clamp terminals)
Outdoor Enclosure: Rittal SK 3321 (IP66, -40°C to 75°C)
For AI deployments:
Use TensorFlow Lite Delegates for NPU acceleration
Enable MIPI-CSI HDR mode for high-contrast environments
Integrate Azure Percept for cloud-model orchestration
