oxhc/splitplace

[TMC'22] SplitPlace: AI Augmented Splitting and Placement of Large-Scale Neural Networks in Mobile Edge Environments

SplitPlace Framework

SplitPlace is a container orchestration framework for dynamic scheduling and decision making in resource constrained edge environments. SplitPlace decides whether to use semantic or layer wise splits of neural network applications with latency and accuracy critical user requirements on distributed setups with low memory legacy devices.

Quick Start Guide

SplitPlace is based on the COSCO Framework and uses the co-simulation and surrogate optimization primitives of COSCO. To run the framework, install required packages using

python3 install.py

To run the code with the required scheduler, modify lines 81 and 85 of main.py to one of the several options.

decider = MABDecider()
...
scheduler = GOBIScheduler('energy_latency_'+str(HOSTS))

To run the simulator, use the following command

python3 main.py

Wiki

Access the wiki for installation instructions and replication of results.

Links

Items	Contents
Paper	https://ieeexplore.ieee.org/document/9780535
Pre-print	https://arxiv.org/pdf/2205.10635.pdf
Documentation	https://github.com/imperial-qore/COSCO/wiki
Video	(coming soon)
Contact	Shreshth Tuli (@shreshthtuli)
Funding	Imperial President's scholarship, H2020-825040 (RADON)

Cite this work

Our work is published in IEEE TMC journal. Cite using the following bibtex entry.

@article{tuli2021splitplace,
  author={Tuli, Shreshth and Casale, Giuliano and Jennings, Nicholas R.},
  journal={IEEE Transactions on Mobile Computing}, 
  title={{SplitPlace: AI Augmented Splitting and Placement of Large-Scale Neural Networks in Mobile Edge Environments}}, 
  year={2022}
}

License

BSD-3-Clause. Copyright (c) 2020, Shreshth Tuli. All rights reserved.

See License file for more details.