Jetson Nano: Nvidia Machine Learning Single Board

    Yesterday, Nvidia announced the Jetson Nano : A Single Board AI Computing Computer. A small computer with CUDA-X AI library support delivers 472 gigaflops to run modern AI workloads while consuming just 5 watts.

    A single-board player was presented at the GPU Technology Conference, and the presentation was made by Nvidia founder and CEO Jensen Huang.

    Technical specifications:

    • GPU: Nvidia with Maxwell architecture with 128 CUDA cores
    • Processor: Quad-core ARM Cortex-A57 MPCore
    • Video: 4K at 30 frames per second (H.264 / H.265 format) and 4K at 60 frames per second (H.264 / H.265 format) for encoding and decoding, respectively
    • Video Output: HDMI 2.0 or DP1.2 | eDP 1.4 | DSI (1 × 2), two at the same time
    • RAM: 4 GB LPDDR4 64-bit; 25.6 GB / s
    • Flash memory: 16 GB eMMC
    • Camera: 12 lines (3 × 4 or 4 × 2) MIPI CSI-2 DPHY 1.1 (1.5 Gbit / s), 12x (module) and 1x (developer kit)
    • Connectors: 1 × 1/2/4 PCIE, 1 × USB 3.0, 3 × USB 2.0
    • I / O: 1 × SDIO / 2 × SPI / 6 × I2C / 2 × I2S / GPIO pins
    • Network: Gigabit Ethernet
    • OS Support: Linux for Tegra
    • Module size: 69.5 × 45 mm
    • Devkit size: 100 × 80 mm
    • Connection: 260-pin connector

    Jetson Nano comes in two versions:

    1. Devkit for developers, manufacturers and enthusiasts for $ 99;
    2. a ready-made module for companies wishing to create systems for the mass market for $ 129.

    Jetson Nano supports high-resolution sensors, can simultaneously process information from multiple sensors and run multiple neural networks at the same time. It also supports many popular AI frameworks, which allows developers to integrate their favorite models and frameworks.

    According to Nvidia, a cheap single-board card is “perfect for businesses, startups and researchers,” who previously could not afford to buy more expensive boards. Thus, the Jetson platform significantly expands its audience, and the AI ​​accelerator actually becomes almost a consumer product. At least this maker can buy this maker. According to Nvidia, the board “brings the power of modern AI to an inexpensive platform, stimulating a new wave of innovation from manufacturers, inventors, developers and students. They can create AI projects that were previously impossible, and take existing projects to a new level - mobile robots and drones, digital assistants, automated devices and much more. ”

    The kit comes with support for full desktop Linux, is compatible with many popular peripherals and accessories. Reference books are also available to help you figure this out. In extreme cases, you can ask a question on the Jetson developers forum , where colleagues will answer technical questions.

    This is not the first Nvidia product in the Jetson family, which also includes the powerful Jetson AGX Xavier system for stand-alone machines and Jetson TX2 for embedded applications (AI at the Edge).

    Jetson AGX Xavier

    For comparison, the Jetson AGX Xavier runs on a 512-core Volta GPU with tensor cores, there is a deep learning accelerator, 16 GB of memory, a special computer vision accelerator (7-Way VLIW Vision Processor), the ability to encode video with a resolution of up to 8K and decode simultaneously up to 12 streams of 4K. Much of this is missing from the Jetson Nano mono board. But then it is much smaller and cheaper than the Jetson AGX Xavier: only 69.6 × 45 mm against 87 × 100 mm and the price is $ 99 against $ 1299. As they say, feel the difference.

    The third member of the Jetson TX2 family comes in three versions: TX2 (8GB), TX2 4GB and TX2i. They also differ in the amount of flash memory (16-32 GB) and the presence of built-in Wi-Fi, which is only in TX2 (8GB).

    Jetson TX2

    Jetson TX2 runs a Pascal GPU with 256 Nvidia CUDA cores. In other specifications and size (87 × 50 mm), the Jetson TX2 board is similar to the Jetson Nano, but it costs significantly more: around $ 600 (devkit). So Jetson Nano is cheaper than hundreds of dollars - this is really something special for Nvidia.

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