In-network machine learning using programmable network devices: A survey
Machine learning is widely used to solve networking challenges, ranging from traffic
classification and anomaly detection to network configuration. However, machine learning …
classification and anomaly detection to network configuration. However, machine learning …
Delocalized photonic deep learning on the internet's edge
Advanced machine learning models are currently impossible to run on edge devices such
as smart sensors and unmanned aerial vehicles owing to constraints on power, processing …
as smart sensors and unmanned aerial vehicles owing to constraints on power, processing …
Lightning: A reconfigurable photonic-electronic smartnic for fast and energy-efficient inference
The massive growth of machine learning-based applications and the end of Moore's law
have created a pressing need to redesign computing platforms. We propose Lightning, the …
have created a pressing need to redesign computing platforms. We propose Lightning, the …
Automating in-network machine learning
Using programmable network devices to aid in-network machine learning has been the
focus of significant research. However, most of the research was of a limited scope …
focus of significant research. However, most of the research was of a limited scope …
IIsy: Practical in-network classification
C Zheng, Z Xiong, TT Bui, S Kaupmees… - arXiv preprint arXiv …, 2022 - arxiv.org
The rat race between user-generated data and data-processing systems is currently won by
data. The increased use of machine learning leads to further increase in processing …
data. The increased use of machine learning leads to further increase in processing …
pforest: In-network inference with random forests
C Busse-Grawitz, R Meier, A Dietmüller… - arXiv preprint arXiv …, 2019 - arxiv.org
When classifying network traffic, a key challenge is deciding when to perform the
classification, ie, after how many packets. Too early, and the decision basis is too thin to …
classification, ie, after how many packets. Too early, and the decision basis is too thin to …
On-fiber photonic computing
In the 1800s, Charles Babbage envisioned computers as analog devices. However, it was
not until 150 years later that a Mechanical Analog Computer was constructed for the US …
not until 150 years later that a Mechanical Analog Computer was constructed for the US …
IIsy: Hybrid In-Network Classification Using Programmable Switches
C Zheng, Z Xiong, TT Bui, S Kaupmees… - IEEE/ACM …, 2024 - ieeexplore.ieee.org
The soaring use of machine learning leads to increasing processing demands. As data
volume keeps growing, providing classification services with good machine learning …
volume keeps growing, providing classification services with good machine learning …
Netcast: low-power edge computing with WDM-defined optical neural networks
This paper analyzes the performance and energy efficiency of Netcast, a recently proposed
optical neural-network architecture designed for edge computing. Netcast performs deep …
optical neural-network architecture designed for edge computing. Netcast performs deep …
In-Line Any-Depth Deep Neural Networks Using P4 Switches
In-network function offloading using programmable data plane languages like P4 offers
computational resource savings and efficient operations at the network edge. However, the …
computational resource savings and efficient operations at the network edge. However, the …