Online Spatio-Temporal Learning with Target Projection

Thomas Ortner, Lorenzo Pes, Joris Gentinetta, Charlotte Frenkel, Angeliki Pantazi

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

25 Downloads (Pure)

Abstract

Recurrent neural networks trained with the backpropagation through time (BPTT) algorithm have led to astounding successes in various temporal tasks. However, BPTT introduces severe limitations, such as the requirement to propagate information backwards through time, the weight symmetry requirement, as well as update-locking in space and time. These problems become roadblocks for AI systems where online training capabilities are vital. Recently, researchers have developed biologically-inspired training algorithms, addressing a subset of those problems. In this work, we propose a novel learning algorithm called online spatio-temporal learning with target projection (OSTTP) that resolves all aforementioned issues of BPTT. In particular, OSTTP equips a network with the capability to simultaneously process and learn from new incoming data, alleviating the weight symmetry and update-locking problems. We evaluate OSTTP on two temporal tasks, showcasing competitive performance compared to BPTT. Moreover, we present a proof-of-concept implementation of OSTTP on a memristive neuromorphic hardware system, demonstrating its versatility and applicability to resource-constrained AI devices.

Original languageEnglish
Title of host publicationAICAS 2023 - IEEE International Conference on Artificial Intelligence Circuits and Systems, Proceeding
PublisherIEEE
ISBN (Electronic)9798350332674
DOIs
Publication statusPublished - 2023
Event5th IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2023 - Hangzhou, China
Duration: 11 Jun 202313 Jun 2023

Publication series

NameAICAS 2023 - IEEE International Conference on Artificial Intelligence Circuits and Systems, Proceeding

Conference

Conference5th IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2023
Country/TerritoryChina
CityHangzhou
Period11/06/2313/06/23

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

  • bio-inspired training
  • neuromorphic hardware
  • Online learning
  • phase-change memory
  • update locking

Fingerprint

Dive into the research topics of 'Online Spatio-Temporal Learning with Target Projection'. Together they form a unique fingerprint.

Cite this