EnfangCui's repositories
autogluon
AutoGluon: AutoML for Text, Image, and Tabular Data
Awesome-Dataset-Distillation
Awesome Dataset Distillation Papers
awesome-serverless-papers
Collect papers about serverless computing research
community
KubeEdge community relevant content
cva6_ebpf
The CORE-V CVA6 is an Application class 6-stage RISC-V CPU capable of booting Linux
Deep-reinforcement-learning-with-pytorch
PyTorch implementation of DQN, AC, ACER, A2C, A3C, PG, DDPG, TRPO, PPO, SAC, TD3 and ....
dongfeng-pay
go，支付系统，聚合支付，四方支付，前后端齐全（管理后台，商户后台，代理后台，网关，代付，等）
Edge-Intelligence
随着移动云计算和边缘计算的快速发展，以及人工智能的广泛应用，产生了边缘智能(Edge Intelligence)的概念。深度神经网络(例如CNN)已被广泛应用于移动智能应用程序中，但是移动设备有限的存储和计算资源无法满足深度神经网络计算的需求。神经网络压缩与加速技术可以加速神经网络的计算，例如剪枝、量化、卷积核分解等。但是这些技术在实际应用非常复杂，并且可能导致模型精度的下降。在移动云计算或边缘计算中，任务卸载技术可以突破移动终端的资源限制，减轻移动设备的计算负载并提高任务处理效率。通过任务卸载技术优化深度神经网络成为边缘智能研究中的新方向。Neurosurgeon: Collaborative Intelligence Between the Cloud and Mobile Edge这篇文章提出了协同推断的**，将深度神经网络进行分区，一部分层在移动端计算，而另一部分在云端计算。根据硬件平台、无线网络以及服务器负载等因素实现动态分区，降低时延以及能耗。本项目给出了边缘智能方面的相关论文，并且给出了一个Python语言实现的卷积神经网络协同推断实验平台。关键词：边缘智能(Edge Intelligence)，计算卸载(Computing Offloading)，CNN模型分区(CNN Partition)，协同推断(Collaborative Inference)，移动云计算(Mobile Cloud Computing)
feddst
Federated Dynamic Sparse Training
Federated-Learning-and-Split-Learning-with-raspberry-pi
SRDS 2020: End-to-End Evaluation of Federated Learning and Split Learning for Internet of Things
fedgs
FedGS: A Federated Group Synchronization Framework Implemented by LEAF-MX.
fpga-network-stack
Scalable Network Stack for FPGAs (TCP/IP, RoCEv2)
hivemq-mqtt-tensorflow-kafka-realtime-iot-machine-learning-training-inference
Real Time Big Data / IoT Machine Learning (Model Training and Inference) with HiveMQ (MQTT), TensorFlow IO and Apache Kafka - no additional data store like S3, HDFS or Spark required
istio
Connect, secure, control, and observe services.
Partial-Computation-Offloading-For-MEC
基于深度强化学习的部分计算任务卸载延迟优化
riscv-lab-access
Access bookkeeping for RISC-V Lab
sedna
AI tookit over KubeEdge
starlight
Fast Container Provisioning on the Edge and over the WAN
test
test
vita
Source codes for VITA published in IEEE INFOCOM 2022