In-Network ML
In-Network ML
In-Network Machine Learning (ML) is the offloading of machine learning tasks to run within network devices, such as network switches or NICs.
Our research focuses on in-network ML on unmodified, off-the-shelf network devices. We have done significant work in this area, such as:
A Survey of in-network ML on programmable network devices - the best place to get introduced to the research area.
IIsy, our first work which introduced the mapping on classic ML algorithms to programmable pipelines. A later version of the work also introduced hybrid-deployment of in-network ML.
Planter, a modular framework for automated in-network ML deployment.
DINC, a modular framework for distributed in-network computing and in-network ML.
P4Pir, a solution for smart traffic analysis on IoT gateways.
FliP4, a distributed in-network attack detection framework based on federated tree models.
LOBIN and Linnet, building limit order books within switches for algorithmic trading.
QCMP, Load Balancing using In-Network Reinforcement Learning within the data plane.
INCS, In-network detection and mitigation of e-commerce bot traffic.
MIND, Transaction fraud detection in real time using in-network ML.
GridWatch, using in-network ML to detect false data injection in smart grids.
And more in the pipeline!
We acknowledge support from VMWare, Intel, NVIDIA, AMD (Xilinx) and EU Horizon/UKRI Guarantee SMARTEDGE project.