A big endian Gentoo port developed on a Pine64.org RockPro64

Related tags

Deep Learninggentooarm64big-endianaarch64-be
Overview

Gentoo-aarch64_be

A big endian Gentoo port developed on a Pine64.org RockPro64

The endian wars are over... little endian won. As a result, it is increasingly difficult to find systems that allow you to test for endian cleanliness... I developed this port to facilitate testing of device drivers and general software on a big endian system. QEMU is another approach for big endian testing, however performance suffers greatly and many race conditions are harder to find. The RockPro64 platform is affordable and unlike other small ARM systems, it supports a PCIe slot for testing hardware add in cards! DISCLAIMER: I am not associated with Pine64 in any way other than as a satisfied customer.

This port has only been tested on the RockPro64 4GB board, however it likely will run on any ARM based system that can boot the Gentoo Arm64 installation CD image.

This repository will be populated over the next few weeks - there are several cleanup activities that I would like to complete before publishing.

Owner
Rory Bolt
Senior fellow at Kioxia America, Inc
Rory Bolt
GitHub Repository
Official repository for Jia, Raghunathan, Göksel, and Liang, "Certified Robustness to Adversarial Word Substitutions" (EMNLP 2019)

Certified Robustness to Adversarial Word Substitutions This is the official GitHub repository for the following paper: Certified Robustness to Adversa

Robin Jia 38 Oct 16, 2022
A Python library for common tasks on 3D point clouds

Point Cloud Utils (pcu) - A Python library for common tasks on 3D point clouds Point Cloud Utils (pcu) is a utility library providing the following fu

Francis Williams 622 Dec 27, 2022
BridgeGAN - Tensorflow implementation of Bridging the Gap between Label- and Reference-based Synthesis in Multi-attribute Image-to-Image Translation.

Bridging the Gap between Label- and Reference based Synthesis(ICCV 2021) Tensorflow implementation of Bridging the Gap between Label- and Reference-ba

huangqiusheng 8 Jul 13, 2022
Keras + Hyperopt: A very simple wrapper for convenient hyperparameter optimization

This project is now archived. It's been fun working on it, but it's time for me to move on. Thank you for all the support and feedback over the last c

Max Pumperla 2.1k Jan 03, 2023
Crowd-sourced Annotation of Human Motion.

Motion Annotation Tool Live: https://motion-annotation.humanoids.kit.edu Paper: The KIT Motion-Language Dataset Installation Start by installing all P

Matthias Plappert 4 May 25, 2020
A copy of Ares that costs 30 fucking dollars.

Finalement, j'ai décidé d'abandonner cette idée, je me suis comporté comme un enfant qui été en colère. Comme m'ont dit certaines personnes j'ai des c

Bleu 24 Apr 14, 2022
Pytorch implementation for "Distribution-Balanced Loss for Multi-Label Classification in Long-Tailed Datasets" (ECCV 2020 Spotlight)

Distribution-Balanced Loss [Paper] The implementation of our paper Distribution-Balanced Loss for Multi-Label Classification in Long-Tailed Datasets (

Tong WU 304 Dec 22, 2022
Plug and play transformer you can find network structure and official complete code by clicking List

Plug-and-play Module Plug and play transformer you can find network structure and official complete code by clicking List The following is to quickly

8 Mar 27, 2022
BanditPAM: Almost Linear-Time k-Medoids Clustering

BanditPAM: Almost Linear-Time k-Medoids Clustering This repo contains a high-performance implementation of BanditPAM from BanditPAM: Almost Linear-Tim

254 Dec 12, 2022
PyExplainer: A Local Rule-Based Model-Agnostic Technique (Explainable AI)

PyExplainer PyExplainer is a local rule-based model-agnostic technique for generating explanations (i.e., why a commit is predicted as defective) of J

AI Wizards for Software Management (AWSM) Research Group 14 Nov 13, 2022
Collision risk estimation using stochastic motion models

collision_risk_estimation Collision risk estimation using stochastic motion models. This is a new approach, based on stochastic models, to predict the

Unmesh 7 Jun 26, 2022
Implementation of "Generalizable Neural Performer: Learning Robust Radiance Fields for Human Novel View Synthesis"

Generalizable Neural Performer: Learning Robust Radiance Fields for Human Novel View Synthesis Abstract: This work targets at using a general deep lea

163 Dec 14, 2022
An off-line judger supporting distributed problem repositories

Thaw 中文 | English Thaw is an off-line judger supporting distributed problem repositories. Everyone can use Thaw release problems with license on GitHu

countercurrent_time 2 Jan 09, 2022
A Fast Sequence Transducer Implementation with PyTorch Bindings

transducer A Fast Sequence Transducer Implementation with PyTorch Bindings. The corresponding publication is Sequence Transduction with Recurrent Neur

Awni Hannun 184 Dec 18, 2022
A Pytorch Implementation of Domain adaptation of object detector using scissor-like networks

A Pytorch Implementation of Domain adaptation of object detector using scissor-like networks Please follow Faster R-CNN and DAF to complete the enviro

2 Oct 07, 2022
python debugger and anti-vm that checks if you're in a virtual machine or if someones trying to debug your file

Anti-Debug was made by Love ❌ code ✅ 🎉 ・What it checks for ・ Kills tools that can be used to debug your file ・ Exits if ran in vm (supports different

Rdimo 31 Aug 09, 2022
A mini library for Policy Gradients with Parameter-based Exploration, with reference implementation of the ClipUp optimizer from NNAISENSE.

PGPElib A mini library for Policy Gradients with Parameter-based Exploration [1] and friends. This library serves as a clean re-implementation of the

NNAISENSE 56 Jan 01, 2023
Exponential Graph is Provably Efficient for Decentralized Deep Training

Exponential Graph is Provably Efficient for Decentralized Deep Training This code repository is for the paper Exponential Graph is Provably Efficient

3 Apr 20, 2022
[NeurIPS 2021] “Improving Contrastive Learning on Imbalanced Data via Open-World Sampling”,

Improving Contrastive Learning on Imbalanced Data via Open-World Sampling Introduction Contrastive learning approaches have achieved great success in

VITA 24 Dec 17, 2022
FS-Mol: A Few-Shot Learning Dataset of Molecules

FS-Mol is A Few-Shot Learning Dataset of Molecules, containing molecular compounds with measurements of activity against a variety of protein targets. The dataset is presented with a model evaluation

Microsoft 114 Dec 15, 2022