An open source Jetson Nano baseboard and tools to design your own.

Related tags

Deep Learninghardware
Overview

My Jetson Nano Baseboard

Render of My Jetson Nano Baseboard

Picture of My Jetson Nano Baseboard

This basic baseboard gives the user the foundation and the flexibility to design their own baseboard for the Jetson Nano. It also repurposes some of the Jetson Nano’s interface signals for simple beginner projects.

This baseboard, as designed, contains:

  • A 5V, 4A DC barrel jack
  • 4 USB 2.0 connectors
  • An HDMI connector
  • A UART-to-USB bridge
  • A debug USB
  • A 40-pin GPIO
  • A servo header
  • Three user-interactive buttons (power, reset, and force recovery)
  • A flex connector for an OLED display

Use these files to kickstart your own application-specific baseboard or implement some quick and easy projects!

Quick Start

You only need a computer to get started right now! For a more in-depth setup guide, check out docs/setup.md.

  1. Install the appropriate version of KiCAD, an open source schematic and layout design program, for your operating system here.

  2. Download this GitHub repository either as a ZIP or on the command line.

  3. Save the following symbol and footprint libraries to the “Design Files/Libraries” folder (you may have to make an account – alternatively, if you want the practice, you could try making them yourself):

    1. B3SL-1002P
    2. TPD4E02B04DQAR
    3. 3-1734592-0
    4. DC-005-2.0A
    5. ACM2012-201-2P-T001
    6. 10029449-111RLF
    7. 2309413-1
  4. Open the project (.pro) file in KiCAD.

  5. Add the symbol and footprint libraries as project-specific to your KiCAD program, following this guide. You will know that the libraries are correctly loaded when there are no more boxes with question marks.

  6. You are now set up to tinker with the files and launch your own hardware designs!

Be sure to download the Jetson Nano Product Design Guide here (NVIDIA Developer account required) to help you with your design.

If you want to make your boards and test them, you’ll need the Jetson Nano module (not included, can be bought as part of the developer kit here). It is also helpful to have electronics equipment like an oscilloscope, a multimeter, and a soldering iron.

Questions and Improvements

If you have a suggestion, please open an issue on GitHub.

Please share your projects with us on the Jetson Developer Forums.

Comments
  • Servo PWM signal does not switch logic levels, remains high

    Servo PWM signal does not switch logic levels, remains high

    Problem When running the sampleproj/servo_pwm.py script, SERVO_PWM remains high, even as the GPIO07 pin outputs a PWM signal.

    Release A00, no modifications

    Observed Behavior Servo PWM remains high even when PWM script is run.

    GPIO07 at 40-pin header: gpio7 configured0

    SERVO_PWM at servo header: gpio 7

    Root Cause Theory The pullup could be too strong; the transistor connection may not be consistent across boards. This is another issue that switching to a larger package transistor should fix.

    Suggested Next Steps

    • Switch to larger package transistor (as in issue #5 & 2)
    • Test additional boards to ensure transistor connection remains consistent
    bug A01 Fix 
    opened by wolframalexa 1
  • Fails sleep/wake software cycle

    Fails sleep/wake software cycle

    Problem Board cannot be wake from software; the physical button must be pushed.

    Release A00, no modifications

    Observed Behavior When running the validation/sleep_func.sh script, the device does not wake until the power button is pushed.

    Root Cause Theory An issue with how L4T interacts with the baseboard.

    Suggested Next Steps

    • Probe power logic signals to ensure sequencing is correct
    • Read L4T documentation for power design
    bug 
    opened by wolframalexa 1
  • OLED display does not fit in specified connector

    OLED display does not fit in specified connector

    Problem The specified OLED display does not fit in the specified connector. Users cannot use the display.

    Release A00, no modifications

    Observed Behavior The connector is too small for the display.

    Root Cause Theory Mistakes were made.

    Suggested Next Steps Choose new connector for display.

    bug A01 Fix 
    opened by wolframalexa 1
  • Fan PWM signal not in compliance

    Fan PWM signal not in compliance

    Problem Users cannot use a fan, because the fan PWM signal is not in electrical compliance.

    Observed Behavior GPIO14 provides a nice PWM signal, but the signal becomes less crisp as it goes through the level shifter. In both images below, the blue line is the FAN_PWM_LS node. Yellow: left = GPIO14, right = FAN_PWM_INV.

    image image

    The transistor Q3 should invert the PWM signal, but does not appear to do so.

    Root Cause Theory At 20kHz, it is unlikely the fan signal is switching too fast for the transistor. It may be due to a transistor misalignment; switch from the DMN26D0UFB4-7 to the DMN26D0UT-7, which is in a larger SOT-523 package, to avoid misalignments.

    Suggested Next Steps Ask a more experienced engineer. May need a larger pullup, or more power control.

    bug A01 Fix 
    opened by wolframalexa 1
  • Some USB 2.0 type A ports are not functional

    Some USB 2.0 type A ports are not functional

    Problem Some USB ports on some boards do not respond when a USB mouse or keyboard is plugged into them.

    Observed Behavior When a USB mouse or keyboard is plugged into one of the four type A ports, it occasionally does not work (the pointer does not move, no text appears on screen). This hinders the ability for the user to interact with the display, and to use the USB devices they need.

    Root Cause Theory This is only present on some ports on some boards; it could be a manufacturing or hub chip error.

    Suggested Next Steps

    • Visually check all USB components to ensure there is no damage
    • Ensure hub chip is strapped correctly
    • Ensure USB layout guidelines are followed for signal integrity
    bug A01 Fix 
    opened by wolframalexa 1
  • Low-resolution HDMI (1280x720)

    Low-resolution HDMI (1280x720)

    Problem The maximum HDMI resolution seems to be 1280x720, whereas the Jetson Nano achieves a resolution of 2560x1440. As a result, the display appears zoomed in.

    Release A00, no modifications

    Observed Behavior

    • This is present on all boards, with the same module as on the official baseboard - probably a hardware design issue, and not a software issue
    • The resolution on the official Jetson Nano is twice that of "My Jetson Nano Baseboard"

    Root Cause Theory Resistor values may need to be tuned for better resolutions. Additionally, an EEPROM may be read to confirm HDMI resolution.

    Suggested Next Steps Investigate L4T behavior with regard to HDMI and resistor tuning on CEC line.

    bug A01 Fix 
    opened by wolframalexa 1
  • Power LED does not light up on some boards

    Power LED does not light up on some boards

    Problem On some boards, the power LED D6 does not light, even though the board completes its power-on sequence and has booted normally.

    Release A00, no modifications

    Observed Behavior

    • The LED is the correct direction
    • The board completes its power on sequence and the software functions as expected
    • GPIO04 remains LOW at 0.6V, even though it should be driven HIGH upon power-up
    • There is no measured voltage drop across R32
    • There is an insufficient voltage drop across D6

    Root Cause Theory The gate threshold voltage varies depending on the individual transistor. It could be that this transistor has a higher V_GS and does not turn on when GPIO04 is at 0.6V. Additionally, the transistor Q7 could be misaligned.

    Suggested Next Steps

    • Investigate behavior of GPIO04, which should be HIGH upon power-on. Remove R31 to ensure no loading effects from transistor.
    • Change all transistors DMN26D0UFB4-7 to the DMN26D0UT-7, which is in a larger SOT-523 package, to avoid misalignments.
    bug A01 Fix 
    opened by wolframalexa 1
  • Make silkscreen more readable

    Make silkscreen more readable

    • Increase silkscreen size from 0.5x0.5mm to 0.438x0.7mm to make the text more readable.
    • Add polarity for all ICs and polarized components to aid in soldering
    • Add silkscreen on front for 40-pin header
    enhancement A01 Fix 
    opened by wolframalexa 0
Releases(A01)
  • A01(Aug 12, 2021)

    After having manufactured the boards and validated them, we're fixing some functionality. Here are the changes, which you can read about in our issues:

    • Fix footprints (#11):
      • DC Jack flipped
      • GPIO header flipped (pin 1 should be pin 2)
      • USB footprint with soldermask (#4)
      • New OLED display connector & display (#6)
    • Usability enhancements, with more readable silkscreen (#10)
    • BOM errors:
      • SODIMM connector (#9)
      • HDMI current limiting resistor, which allows for correct resolution (#3)
      • Larger transistor footprint to avoid misalignments (#8, #5)
    • Fix pullup
      • Add pullup to GPIO04 (#2)
    Source code(tar.gz)
    Source code(zip)
  • A00(Jul 28, 2021)

Owner
NVIDIA AI IOT
NVIDIA AI IOT
Replication Package for AequeVox:Automated Fariness Testing for Speech Recognition Systems

AequeVox Replication Package for AequeVox:Automated Fariness Testing for Speech Recognition Systems README under development. Python Packages Required

Sai Sathiesh 2 Aug 28, 2022
Python script that takes an Impulse response .wav and a input .wav to demonstrate audio convolution.

convolver Python script that takes an Impulse response .wav and a input .wav to demonstrate audio convolution. Created by Sean Higley

Sean Higley 1 Feb 23, 2022
PICARD - Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models

This is the official implementation of the following paper: Torsten Scholak, Nathan Schucher, Dzmitry Bahdanau. PICARD - Parsing Incrementally for Con

ElementAI 217 Jan 01, 2023
An unofficial styleguide and best practices summary for PyTorch

A PyTorch Tools, best practices & Styleguide This is not an official style guide for PyTorch. This document summarizes best practices from more than a

IgorSusmelj 1.5k Jan 05, 2023
Official code base for the poster "On the use of Cortical Magnification and Saccades as Biological Proxies for Data Augmentation" published in NeurIPS 2021 Workshop (SVRHM)

Self-Supervised Learning (SimCLR) with Biological Plausible Image Augmentations Official code base for the poster "On the use of Cortical Magnificatio

Binxu 8 Aug 17, 2022
DeepMind Alchemy task environment: a meta-reinforcement learning benchmark

The DeepMind Alchemy environment is a meta-reinforcement learning benchmark that presents tasks sampled from a task distribution with deep underlying structure.

DeepMind 188 Dec 25, 2022
Logsig-RNN: a novel network for robust and efficient skeleton-based action recognition

GCN_LogsigRNN This repository holds the codebase for the paper: Logsig-RNN: a novel network for robust and efficient skeleton-based action recognition

7 Oct 14, 2022
Differentiable Wavetable Synthesis

Differentiable Wavetable Synthesis

4 Feb 11, 2022
BADet: Boundary-Aware 3D Object Detection from Point Clouds (Pattern Recognition 2022)

BADet: Boundary-Aware 3D Object Detection from Point Clouds (Pattern Recognition

Rui Qian 17 Dec 12, 2022
PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO), Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR) and Generative Adversarial Imitation Learning (GAIL).

PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO), Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR)

Ilya Kostrikov 3k Dec 31, 2022
ACV is a python library that provides explanations for any machine learning model or data.

ACV is a python library that provides explanations for any machine learning model or data. It gives local rule-based explanations for any model or data and different Shapley Values for tree-based mod

Salim Amoukou 85 Dec 27, 2022
Implementation of our paper "DMT: Dynamic Mutual Training for Semi-Supervised Learning"

DMT: Dynamic Mutual Training for Semi-Supervised Learning This repository contains the code for our paper DMT: Dynamic Mutual Training for Semi-Superv

Zhengyang Feng 120 Dec 30, 2022
PPO Lagrangian in JAX

PPO Lagrangian in JAX This repository implements PPO in JAX. Implementation is tested on the safety-gym benchmark. Usage Install dependencies using th

Karush Suri 2 Sep 14, 2022
Space Ship Simulator using python

FlyOver Basic space-ship simulator using python How to run? Just double click run.py What modules do i need? All modules that i currently using is bui

0 Oct 09, 2022
Pytorch and Keras Implementations of Hyperspectral Image Classification -- Traditional to Deep Models: A Survey for Future Prospects.

The repository contains the implementations for Hyperspectral Image Classification -- Traditional to Deep Models: A Survey for Future Prospects. Model

Ankur Deria 115 Jan 06, 2023
Simulations for Turring patterns on an apically expanding domain. T

Turing patterns on expanding domain Simulations for Turring patterns on an apically expanding domain. The details about the models and numerical imple

Yue Liu 0 Aug 03, 2021
Code for "Reconstructing 3D Human Pose by Watching Humans in the Mirror", CVPR 2021 oral

Reconstructing 3D Human Pose by Watching Humans in the Mirror Qi Fang*, Qing Shuai*, Junting Dong, Hujun Bao, Xiaowei Zhou CVPR 2021 Oral The videos a

ZJU3DV 178 Dec 13, 2022
Implementation of SwinTransformerV2 in TensorFlow.

SwinTransformerV2-TensorFlow A TensorFlow implementation of SwinTransformerV2 by Microsoft Research Asia, based on their official implementation of Sw

Phan Nguyen 2 May 30, 2022
Set of models for classifcation of 3D volumes

Classification models 3D Zoo - Keras and TF.Keras This repository contains 3D variants of popular CNN models for classification like ResNets, DenseNet

69 Dec 28, 2022
Code and real data for the paper "Counterfactual Temporal Point Processes", available at arXiv.

counterfactual-tpp This is a repository containing code and real data for the paper Counterfactual Temporal Point Processes. Pre-requisites This code

Networks Learning 11 Dec 09, 2022