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The SDK also includes a specialized FLIC framework with a plug-in approach to developing application pipelines including image processing, computer vision, and deep learning.
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The Intel® Movidius™ Myriad™ X VPU ships with a rich SDK that contains all of the software development frameworks, tools, drivers and libraries to implement custom imaging, vision and deep learning applications on Intel® Movidius™ Myriad™ X VPU. Other featured interfaces include USB 3.1 and PCIe* Gen 3. The high-throughput inline ISP ensures streams are processed at high speeds, while new hardware encoders provide support for 4K resolutions at both 30 Hz (H.264/H.265) and 60 Hz (M/JPEG) frame rates. The Intel® Movidius™ Myriad™ X VPU features 16 MIPI lanes, which supports up to 8 HD resolution RGB sensors to be connected directly. Movidius VPUs deliver an optimal balance between programmability and performance at low power. This approach enables outstanding image signal processing (ISP) without the need to make trips to memory for best power efficiency, in addition to interleaved computer vision and deep neural network inference application pipelines, all with a data flow methodology that reduces power by minimizing data movement. The architecture provides a modular approach to configuring imaging and vision workloads because it combines a set of imaging and vision hardware accelerators, such as stereo depth or the Neural Compute Engine, with an array of C-programmable VLIW vector processors, all accessing a common on-chip memory. The Movidius family of VPUs have always provided a unique, flexible architecture for image processing, computer vision, and deep neural networks. 1 Customizable Imaging & Vision Pipelines Based on the Intel® Movidius™ Myriad™ X VPU architecture, the maximum number of Neural Network inference operations per second achievable by the Neural Compute Engine in combination with the 16 SHAVE cores (916 billion operations per second) is more than 10x the maximum number of neural network inference operations per second achievable by the Movidius Myriad 2 VPU’s SHAVE processors (80 billion operations per second) for executing neural network inference. 1 The Neural Compute Engine is integrated as part of the power efficient Movidius VPU architecture, which minimizes power by reducing data movement on-chip. Specifically designed to run deep neural networks at high speed and low power, the Neural Compute Engine enables the Intel® Movidius™ Myriad™ X VPU to reach over 1 TOPS of compute performance on deep neural network inferences. Intel has introduced an entirely new deep neural network processing unit into the Intel® Movidius™ Myriad™ X VPU architecture: the Neural Compute Engine. New Generation of Deep Neural Network Performance 2 With new performance enhancements, the Intel® Movidius™ Myriad™ X VPU is a power efficient solution that brings advanced vision and artificial intelligence applications to devices such as drones, smart cameras, smart home, security, VR/AR headsets, and 360 cameras. As a member of the Movidius VPU family known for ultra-low power consumption, theIntel® Movidius™ Myriad™ X VPU is capable of delivering a total performance of over 4 trillion operations per second (TOPS). Intel® Movidius™ Myriad™ X VPU delivers outstanding performance in computer vision and deep neural network inferencing applications. The centralized on-chip memory architecture allows for up to 400 GB/sec of internal bandwidth, minimizing latency and reducing power consumption by minimizing off-chip data transfer.
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For example, the new stereo depth accelerator can simultaneously process 6 camera inputs (3 stereo pairs) each running 720p resolution at 60 Hz frame rate. Utilize over 20 hardware accelerators to perform tasks such as optical flow and stereo depth without introducing additional compute overhead. Run multiple concurrent imaging and vision application pipelines with the flexibility of 16 vector processors optimized for computer vision workloads.Ĭonnect up to 8 HD resolution RGB cameras directly to the Intel® Movidius™ Myriad™ X VPU with support for up to 700 million pixels per second of image signal processing throughput. 1 Run deep neural networks in real time at the edge without compromising on power consumption or accuracy.ġ6 Programmable 128-bit VLIW Vector Processors With this dedicated on-chip accelerator for deep neural networks, the Intel® Movidius™ Myriad™ X VPU delivers over 1 trillion operations per second of DNN inferencing performance.