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 delivers 472 GFLOPS of computing performance with a quad-core 64-bit ARM CPU and a 128-core integrated NVIDIA GPU. It also includes 4GB LPDDR4 memory in an efficient, low-power package with 5W/10W power modes and 5V DC input, as shown below.
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. 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 devkit boots from a removable MicroSD card which can be formatted and imaged from any PC with an SD card adapter. The devkit can be conveniently powered via either the Micro USB port or a 5V DC barrel jack adapter. The camera connector is compatible with affordable MIPI CSI sensors including modules based on the 8MP IMX219, available from Jetson ecosystem partners. Also supported is the Raspberry Pi Camera Module v2, which includes driver support in JetPack. Table 1 shows
The devkit is built around a 260-pin SODIMM-style System-on-Module (SoM), shown in figure 2. The SoM contains the processor, memory, and power management circuitry. The Jetson Nano
Deep Learning Inference Benchmarks
Jetson Nano can run a wide variety of advanced networks, including the full native versions of popular ML frameworks like TensorFlow, PyTorch, Caffe/Caffe2, Keras, MXNet, and others. These networks can be used to build autonomous machines and complex AI systems by implementing robust capabilities such as image recognition, object detection and localization, pose estimation, semantic segmentation, video enhancement, and intelligent analytics.
Figure 3 shows results from inference benchmarks across popular models available online. The inferencing used batch size 1 and FP16 precision, employing NVIDIA’s TensorRT accelerator library included with JetPack 4.2. Jetson Nano attains real-time performance in many scenarios and is capable of processing multiple high-definition video streams.
Multi-Stream Video Analytics
Jetson Nano processes up to eight HD full-motion video streams in real-time and can be deployed as a low-power edge intelligent video analytics platform for Network Video Recorders (NVR), smart cameras, and IoT gateways. NVIDIA’s DeepStream SDK optimizes the end-to-end inferencing pipeline with ZeroCopy and TensorRT to achieve ultimate performance at the edge and for on-premises servers. The video below shows Jetson Nano performing object detection on eight 1080p30 streams simultaneously with a ResNet-based model running at full resolution and a throughput of 500 megapixels per second (MP/s).