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EyeCloudAI CDK is an open source edge-AI camera development kit for applications where privacy, real-time performance, reliability, and flexibility are paramount. With EyeCloudAI CDK, developing and deploying a custom edge-AI camera solution easy — we’ve taken care of the AI optimization, sensor tuning, optics, tooling, and supply chain management so you can focus on your application.
The Core Board is based on an Intel Movidius Myriad X VPU and is compatible with the Intel Distribution of OpenVINO ToolKit. Pair a Core Board with any of three image signal processor (ISP)-ready sensor modules: a 2 MP rolling shutter module for normal applications, an 8 MP rolling shutter module for high-resolution applications, and a 2 MP global shutter module (based on the ON Semi AR0234) for high-speed applications.
A fully assembled EyeCloud CDK system with sensor board and core board with eMMC
|VPU||Intel Movidius Myriad X MA2085|
|Vector Processors||16 SHAVEs|
|Neural Network Capability||Two neural compute engines (up to 4 TOPS)|
|RAM||8 Gbits, LPDDR4 (1600 MHz, 32-bit)|
|Power Supply||5V / 2A|
|Input Data Interface||MIPI 4 lane|
|Output Data Interface||USB 3.0 / USB 2.0 (selectable with cable orientation)|
|Expansion Interfaces||GPIO, I²C, SPI, SD/SDIO, UART, 2 x two-lane DSI (up to 2.5 Gbps per lane)|
|Dimensions||38 mm x 38 mm|
|Cable||30 cm USB Type-C|
|2MRS Sensor Module||2MGS Sensor Module||8MRS Sensor Module|
|Summary||2 Megapixel Rolling Shutter||2 Megapixel Global Shutter||8 Megapixel Rolling Shutter|
|Sensor||SmartSens SC200AI||ON Semi AR0234CS||SmartSens SC8238H|
|Resolution||1920 x 1080 (2K)||1920 × 1080 (2K)||3872 × 2180 (4K)|
|Frame Rate||30 fps||120 fps @ 1 MP, 60 fps @ 2 MP||30 fps|
|Field of View||Diagonal 60°||Diagonal 63°||Diagonal 58°|
|Output Data Interface||MIPI 2 lane||MIPI 4 lane||MIPI 4 lane|
|Dimensions||38 mm x 38 mm||38 mm x 38 mm||38 mm x 38 mm|
|Weight||12 g||12 g||12 g|
|Cable||4 cm FFC||4 cm FFC||4 cm FFC|
EyeCloudAI CDK works with Intel’s distribution of OpenVINO ToolKit and is compatible with frameworks such as ONNX, TensorFlow, Caffee, MXNet, Kaldi, and PaddlePaddle. The SDK is compatible with Raspberry Pi flavors of Linux for ARMv7 and ARMv8.