Sustainable Machine Learning on Edge Device Clusters (Sommersemester 2020)

Lecturer: Dr. Thorsten Papenbrock (Information Systems) , Phillip Wenig (Information Systems)
Course Website: https://hpi.de/en/naumann/teaching/teaching/ss-20/sustainable-machine-learning-on-edge-device-clusters.html

General Information

• Weekly Hours: 4
• Credits: 6
• Graded: yes
• Enrolment Deadline: 06.04.2020 - 22.04.2020
• Teaching Form: Project / Seminar
• Enrolment Type: Compulsory Elective Module
• Course Language: English
• Maximum number of participants: 8

Programs, Module Groups & Modules

Description

In this project seminar, we develop a prototype system that runs end-to-end machine learning pipelines on clusters of edge devices with limited compute power to encourage sustainable hardware usage. For more details about the project and the seminar setup, please have a look at our official project website.

IMPORTANT NOTE: Due to the current COVID-19 situation, we need to start this seminar in online-mode. This means that we use jitsi web-meetings for on-boarding and our regular group sessions. Please find the detailes about our organization on our official project website.

Requirements

All participants need to be familiar with the actor programming model and the Akka toolkit. At best, you have already taken the lecture Distributed Data Management; otherwise, you need to catch up on actor programming.

Examination

The grading will be based on the following tasks:

• (10%) Active participation during all seminar events.
• (60%) Research and development success w.r.t. your pipeline modules including:
  • (20%) Implementation
  • (20%) Evaluation
  • (20%) Paper writing (~1.5 pages per person)
• (30%) Presentations including:
  • (15%) Midterm presentation
  • (15%) Final presentation

Dates

• Weekly meetings
• Time: Tuesdays, 9:15 - 10:45 AM
• Location: F-2.10, Building F, 2nd Floor, Campus II
  Online meetings during the COVID-19 lockdown.

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