Internet of computer vision things Movidius


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  • About Movidius

    Share this for more tutorials and computer vision post from me.. Thanks best Vladimir

    Movidius Computer Vision

    Movidius is interesting company which developed embedded machine vision technology for new segment of fast growing smartphones and IoT apps. Smart devices in internet of things needs smart processing units like this for 3D depth application, reconstruction, detection and Natural user interface. Near future brings challenging task like smart mirror and communication walls and many more "disturbing things". Movidius provides optimized library for algorithm in modern deep learning in combination with low-power visual processing units. 

    Target Computer vision app 

    • 3D modeling and reconstruction in smart devices of eye of thinks apps
    • In door navigation assistant
    • Visual analysis 
    • Augmented reality
    • Recognition and classification apps


    Movius Vision solutions

    Machine vision algorithms

    Deep learning algorithm is adapted to low power smart processor by low level matrix computation kernels, on chip memory flow management and native mixed precision floating point computation, which dramatically increase the unit performance.

    3D Depth, Depth sensor in common use started by Kinect and today it could be small as notebook integrated camera (Intel have one). Applications are in augmented reality, 3D reconstruction and also detection. 

    Tracking and Detection Object tracking and detection are great challenge in surveillance applications, indoor tracking and navigation and object detection. One of the interesting application is drones and autonomous robot navigation to catch target on cameras. 

    Dev support

    • Set of optimized computer vision and math libs
    • C, C++ and OpenCL
    • Eclipse graphical development
    • Posix RTOS

    Vision Processing Unit

    Processor is based on Streaming Hybrid Architecture Vector Engines (SHAVE). This processor achieve the hundreds of GFLOPS in matrix multiplication in deep learning networks of various topologies.

    There is some specifications of Myriad 2 SoC

    • Two trillion 16 bits operation with 500 mW of power usage
    • 12x 128-bit vector VLIW SHAVE type Processor for vision apps
    • 2x 32-bit RISC processor
    • 16 and 32 bit floating point operations
    • 8/16/32 bit integer operations
    • 2MB on chip memory



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