2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
Login Paper Search My Schedule Paper Index Help

My ICASSP 2021 Schedule

Note: Your custom schedule will not be saved unless you create a new account or login to an existing account.
  1. Create a login based on your email (takes less than one minute)
  2. Perform 'Paper Search'
  3. Select papers that you desire to save in your personalized schedule
  4. Click on 'My Schedule' to see the current list of selected papers
  5. Click on 'Printable Version' to create a separate window suitable for printing (the header and menu will appear, but will not actually print)

Paper Detail

Paper IDSS-15.1
Paper Title COLLABORATIVE INFERENCE VIA ENSEMBLES ON THE EDGE
Authors Nir Shlezinger, Ben-Gurion University of the Negev, Israel; Erez Farhan, Hai Morgenstern, BeyondMinds, Israel; Yonina C. Eldar, Weizmann Institute of Science, Israel
SessionSS-15: Signal Processing for Collaborative Intelligence
LocationGather.Town
Session Time:Friday, 11 June, 13:00 - 13:45
Presentation Time:Friday, 11 June, 13:00 - 13:45
Presentation Poster
Topic Special Sessions: Signal Processing for Collaborative Intelligence
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract The success of deep neural networks (DNNs) as an enabler of artificial intelligence (AI) is heavily dependent on high computational resources. The increasing demands for accessible and personalized AI give rise to the need to operate DNNs on edge devices such as smartphones, sensors, and autonomous cars, whose computational powers are limited. Here we propose a framework for facilitating the application of DNNs on the edge in a manner which allows multiple users to collaborate during inference in order to improve their prediction accuracy. Our mechanism, referred to as edge ensembles, is based on having diverse predictors at each device, which can form a deep ensemble during inference. We analyze the latency induced in this collaborative inference approach, showing that the ability to improve performance via collaboration comes at the cost of a minor additional delay. Our experimental results demonstrate that collaborative inference via edge ensembles equipped with compact DNNs substantially improves the accuracy over having each user infer locally, and can outperform using a single centralized DNN larger than all the networks in the ensemble together.