Raspberry Pi-style Jetson Nano is a powerful low-cost AI computer from Nvidia

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The $99 Jetson Nano Developer Package is a board tailor-made for working machine-learning fashions and utilizing them to hold out duties corresponding to laptop imaginative and prescient.

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Builders who wish to use machine studying on home made devices or prototype home equipment simply obtained a strong new low-cost possibility, with Nvidia revealing the Jetson Nano.

The $99 Jetson Nano Developer Package is a board tailor-made for working machine-learning fashions and utilizing them to hold out duties corresponding to laptop imaginative and prescient.

Nvidia has proven the board getting used to spotlight folks and vehicles captured by CCTV streams, working real-time object detection on eight 1080p30 streams concurrently, utilizing a ResNet-based mannequin working at full decision and dealing with a throughput of 500 megapixels per second.

The tiny board packs an Arm-based CPU and Nvidia GPU, based mostly on the 2014 Maxwell structure, which collectively ship 472 GFLOPs of compute efficiency and devour as somewhat as 5 watts.

SEE: Free machine studying programs from Google, Amazon, and Microsoft: What do they provide? (Tech Professional Analysis)

Nvidia launched a sequence of benchmarks exhibiting the Jetson Nano outperforming opponents when working numerous laptop imaginative and prescient fashions. The outcomes present the Jetson Nano beating the $35 Raspberry Pi three (no point out of the mannequin), the Pi three with a $90 Intel Neural Compute Stick 2, and the newly launched Google Coral board that makes use of the Edge TPU (Tensor Processing Unit). These assessments concerned working a spread of laptop imaginative and prescient fashions finishing up object detection, classification, pose estimation segmentation and picture processing. Particularly, the Jetson confirmed superior efficiency when working inference on skilled ResNet-18, ResNet-50, Inception V4, Tiny YOLO V3, OpenPose, VGG-19, Tremendous Decision, and Unet fashions.

The Jetson Nano was the one board to have the ability to run most of the machine-learning fashions and the place the opposite boards may run the fashions, the Jetson Nano usually supplied many occasions the efficiency of its rivals.

Nvidia’s senior supervisor of product for autonomous machines Jesse Clayton instructed TechRepublic’s sister web site ZDNet that Jetson Nano’s GPU may run a broader vary of machine-learning fashions than the specialist silicon present in Google’s Edge TPU.

Nonetheless, it wasn’t a clear sweep for the Jetson Nano, with Google’s Coral board beating the Jetson Nano when working a skilled SSD Mobilenet-V2 mannequin dealing with 300×300 decision photographs, with the Coral capable of run at 48 frames per second (FPS), in comparison with 39FPS on the Jetson Nano.

The assessments above are additionally Nvidia-supplied benchmarks, and in Google’s personal testing of the Coral board it claimed the flexibility to “run MobileNet v2 at 100+ FPS, in an influence environment friendly method”. On-line posts are additionally beginning to emerge from Google Coral homeowners claiming it ought to outperform the Jetson Nano when working MobileNet v2 by a higher margin than Nvidia is claiming.

The Jetson Nano may also be used to coach machine-learning fashions, giving it a bonus over Google’s Edge board, which additionally requires you to add your mannequin to Google for compilation. Coaching efficiency is prone to be restricted, nonetheless, given the price of the board, significantly in comparison with utilizing dearer PC GPUs or cloud-based GPU arrays, and Nvidia itself says that coaching ought to solely be carried out by those that are “keen to attend longer for outcomes”.

The Jetson Nano is constructed round a quad-core 64-bit Arm-based CPU, a 128-core built-in Nvidia GPU and 4GB LPDDR4 reminiscence.

The brand new JetPack four.2 SDK gives a whole desktop Linux surroundings for the board, based mostly on Ubuntu 18.04, with the OS bundling the NVIDIA CUDA Toolkit 10.zero, and libraries corresponding to cuDNN 7.three and TensorRT 5.

The SDK consists of the flexibility to natively set up well-liked open supply ML frameworks, corresponding to TensorFlow, PyTorch, Caffe, Keras, and MXNet, together with frameworks for laptop imaginative and prescient and robotics growth like OpenCV and ROS.

Nvidia says a spread of peripherals might be hooked as much as the Jetson Nano by way of its ports and GPIO header, such the 3D-printable deep studying JetBot that NVIDIA has open-sourced on GitHub, whereas the Raspberry Pi Digital camera Module v2 can also be supported and might be linked to the board’s MIPI CSI-2 port.

For these getting began with machine studying on the Jetson Nano, Nvidia presents the Hi there AI World information, which it says will permit new customers to have skilled real-time picture classification and object detection working on the board inside a “couple of hours”.

Companies wanting to construct the Jetson Nano right into a completed product can purchase it in a 70 x 45mm System on Module (SOM) kind issue.

The 260-pin SODIMM-style SOM will begin delivery in June 2019 for $129 — for an 1000-unit order. The production-targeted compute module will embrace 16GB eMMC onboard storage and enhanced I/O with PCIe Gen2 x4/x2/x1, MIPI DSI, extra GPIO, and 12 lanes of MIPI CSI-2 for connecting as much as three x4 cameras or as much as 4 cameras in x4/x2 configurations.

Jetson Nano Developer Package specs

CPU 64-bit Quad-core ARM A57 @ 1.43GHz
GPU 128-core NVIDIA Maxwell @ 921MHz
Reminiscence 4GB 64-bit LPDDR4 @ 1600MHz | 25.6 GB/s
Video Encoder* 4Kp30 | (4x) 1080p30 | (2x) 1080p60
Video Decoder* 4Kp60 | (2x) 4Kp30 | (8x) 1080p30 | (4x) 1080p60
USB 4x USB three.zero A (Host) | USB 2.zero Micro B (Gadget)
Digital camera MIPI CSI-2 x2 (15-position Flex Connector)
Show HDMI | DisplayPort
Networking Gigabit Ethernet (RJ45)
Wi-fi M.2 Key-E with PCIe x1
Storage MicroSD card (16GB UHS-1 really helpful minimal)
Different I/O (3x) I2C | (2x) SPI | UART | I2S | GPIOs
nvidiaboard.png

The $99 Jetson Nano Developer Package board.

Picture: Nvidia

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