JETSON NANO - B01 (Revised version with 2 camera ports)
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Jetson Nano - B01 (Revised version with 2 camera ports)
NVIDIA® Jetson Nano™ Developer Kit is a small, powerful computer that lets you run multiple neural networks in parallel for applications like image classification, object detection, segmentation, and speech processing. All in an easy-to-use platform that runs in as little as 5 watts.
Specifications of The New Dev Kit Module & Carrier Board
Main specification for new Jetson Nano Kit
B01 Jetson Nano CoM Module
- 128-core Maxwell GPU
- Quad-core Arm A57 processor @ 1.43 GHz
- System Memory – 4GB 64-bit LPDDR4 @ 25.6 GB/s
- Storage – microSD card slot
- 4x MIPI CSI-2 DPHY 10 lanes in total ( 3x 2-lane + 1x 4-lane)
- Video Encode – 4K @ 30 | 4x 1080p @ 30 | 9x 720p @ 30 (H.264/H.265)
- Video Decode – 4K @ 60 | 2x 4K @ 30 | 8x 1080p @ 30 | 18x 720p @ 30 (H.264/H.265)
- Dimensions – 70 x 45 mm
B01 Carrier Board
- 260-pin SO-DIMM connector for Jetson Nano module.
- Video Output – HDMI 2.0 and eDP 1.4 (video only)
- Connectivity – Gigabit Ethernet (RJ45) + 4-pin PoE header
- USB – 4x USB 3.0 ports, 1x USB 2.0 Micro-B port for power or device mode
- Camera I/F – 2x MIPI CSI-2 DPHY lanes compatible with Raspberry Pi Camera Module V2
- M.2 Key E socket (PCIe x1, USB 2.0, UART, I2S, and I2C) for wireless networking cards
- 40-pin expansion header with GPIO, I2C, I2S, SPI, UART signals
- 8-pin button header with system power, reset and force recovery-related signals
- Misc – Power LED, 4-pin fan header
- Power Supply – 5V/4A via power barrel or 5V/2A via micro USB port selectable by jumper; optional PoE support
- Dimensions – 100 x 80 x 29 mm (with heatsink)
Changes and Issues with The B01 Kit
The most impressive highlight of the update is the second camera port. The B01 carrier board exposes another MIPI CSI-2 camera interface, which makes it ready for binocular applications like stereo recording, depth sensing, 3D object tracking and image stitching, etc.
That means a lot for Arducam. We are committed to embedded camera solutions, and on the Jetson Nano platform, we’ve offered Jetson Nano stereo camera applications before it even had a second camera. Now that it comes with an extra camera interface, it will not only support our previous binocular applications like depth mapping, but also open up the possibilities to interface even more cameras with Arducam multi-camera solutions like stereo HAT and multi-camera adapter.
To make room for the second camera port, some connectors are either moved elsewhere or removed. Other changes include the removal of the “button” and serial headers, and the power select header (J48) has been moved to the edge of the board.
Removed Serial Port Header (J44)
Changed the Button header (J40) under the module.
Changed the position of Power Select Header (J48)
Added a camera slot (J13)
Although there are hardware changes to the Jetson Nano B01, the supported accessories will continue to work with this platform. We’ve finished the test of Arducam IMX219 camera series and MIPI camera module series on the B01, and we believe it will get better performance with our continuous works.
- The Jetson Nano A02 version of the developer kit hardware cannot boot with Intel 8260 WiFi plugged in. This issue is fixed in the newer B01 version of the hardware https://developer.nvidia.com/tegra-linux-driver-package-release-notes-r3222-ga
- B0x images will work on A02 boards without issue. There are some pinmux differences between the two hardware revisions, but the B0x pinmux settings will work fine on an A02 board. However, A0x pinmux (dtb) does not work on B0x board.
Rev A02 (Old Kit) Vs Rev B01 (New Kit)
Other than the major hardware changes of the carrier board in adding support to the second camera and production module, there are almost no updates in the computing power of the module. Anyway, the CPU and GPU remain the same.
Despite the same computing power, the software packages will still be constantly updated to unleash its full potential. Jetpack 4.3 for Jetson Nano is released with the new TensorRT 6.0.1 and cuDNN 7.6.3 libraries, which helps improve the AI inference performance by 25%. The VPI (Vision Programming Interface) accelerates 4K video or multiple 1080P video feeds (up to 8x at the same time) using GPU+CPU hardware encoder/decoder, and with ML (Machine Learning) algorithms it can do multi-task of image detection, recognition, and tracking.