The SoM contains the processor, memory, and power management circuitry. The devkit is built around a 260-pin SODIMM-style System-on-Module (SoM), shown in figure 2. MicroSD card (16GB UHS-1 recommended minimum) MIPI CSI-2 x2 (15-position Flex Connector) *Indicates maximum number of concurrent streams up to the aggregate throughput. Jetson Nano Developer Kit technical specifications. Also supported is the Raspberry Pi Camera Module v2, which includes driver support in JetPack. The camera connector is compatible with affordable MIPI CSI sensors including modules based on the 8MP IMX219, available from Jetson ecosystem partners. The devkit can be conveniently powered via either the Micro USB port or a 5V DC barrel jack adapter. The devkit boots from a removable MicroSD card which can be formatted and imaged from any PC with an SD card adapter. The ports and GPIO header works out-of-the-box with a variety of popular peripherals, sensors, and ready-to-use projects, such as the 3D-printable deep learning JetBot that NVIDIA has open-sourced on GitHub. The Jetson Nano Developer Kit fits in a footprint of just 80x100mm and features four high-speed USB 3.0 ports, MIPI CSI-2 camera connector, HDMI 2.0 and DisplayPort 1.3, Gigabit Ethernet, M.2 Key-E module, MicroSD card slot, and 40-pin GPIO header. Even transfer learning is possible for re-training networks locally onboard Jetson Nano using the ML frameworks. These capabilities enable multi-sensor autonomous robots, IoT devices with intelligent edge analytics, and advanced AI systems. Jetson Nano brings real-time computer vision and inferencing across a wide variety of complex Deep Neural Network (DNN) models. The newly released JetPack 4.2 SDK provides a complete desktop Linux environment for Jetson Nano based on Ubuntu 18.04 with accelerated graphics, support for NVIDIA CUDA Toolkit 10.0, and libraries such as cuDNN 7.3 and TensorRT 5.The SDK also includes the ability to natively install popular open source Machine Learning (ML) frameworks such as TensorFlow, PyTorch, Caffe, Keras, and MXNet, along with frameworks for computer vision and robotics development like OpenCV and ROS.įull compatibility with these frameworks and NVIDIA’s leading AI platform makes it easier than ever to deploy AI-based inference workloads to Jetson. It also includes 4GB LPDDR4 memory in an efficient, low-power package with 5W/10W power modes and 5V DC input, as shown in figure 1. Jetson Nano delivers 472 GFLOPS of compute performance with a quad-core 64-bit ARM CPU and a 128-core integrated NVIDIA GPU. NVIDIA announced the Jetson Nano Developer Kit at the 2019 NVIDIA GPU Technology Conference (GTC), a $99 computer available now for embedded designers, researchers, and DIY makers, delivering the power of modern AI in a compact, easy-to-use platform with full software programmability. Jetson Nano Developer Kit (80x100mm), available now for $99 Both webinars are roughly an hour long and feature a Q&A session at the end. A Jetbot webinar has Python GPIO library tutorials and information on how to train neural networks and perform real-time object detection with JetBot. The Jetson Nano webinar discusses how to implement machine learning frameworks, develop in Ubuntu, run benchmarks, and incorporate sensors. Update: Jetson Nano and JetBot webinars. We’ve received a high level of interest in Jetson Nano and JetBot, so we’re hosting two webinars to cover these topics.
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