string(10) "newsevents"

The Institute of Informatics & Telecommunications, in the context of informative talks open to the public, welcomed speaker Dr Anastasios Kyrillidis, Noah Harding Assistant Professor at the Computer Science department at Rice University. Rice University is one of the Universities in the USA that collaborates with IIT in regard to the offered PhD scholarships.

The talk was titled Distributed neural network training via independent subnets and took place in hybrid mode at NCSR Demokritos at the IIT Lecture Room, on 4 May 2022.

Watch the video of the talk here:


Abstract: Distributed machine learning (ML) can bring more computational resources to bear than single-machine learning, thus enabling reductions in training time. Distributed learning partitions models and data over many machines, allowing model and dataset sizes beyond the available compute power and memory of a single machine. In practice though, distributed ML is challenging when distribution is mandatory, rather than chosen by the practitioner. In such scenarios, data could unavoidably be separated among workers due to limited memory capacity per worker or even because of data privacy issues. Existing distributed methods will utterly fail due to dominant transfer costs across workers, or do not even apply.

We propose a new approach to distributed fully connected neural network learning, called independent subnet training (IST), to handle these cases. In IST, the original network is decomposed into a set of narrow subnetworks with the same depth. These subnetworks are then trained locally before parameters are exchanged to produce new subnets and the training cycle repeats. Such a naturally “model parallel’’ approach limits memory usage by storing only a portion of network parameters on each device. Additionally, no requirements exist for sharing data between workers (i.e., subnet training is local and independent) and communication volume and frequency are reduced by decomposing the original network into independent subnets. These properties of IST can cope with issues due to distributed data, slow interconnects, or limited device memory, making IST a suitable approach for cases of mandatory distribution. This talk will provide results on MLPs, ResNets, CNNs, efficient pretraining tasks, GCNs as well as some theoretical guarantees.

Bio: My name is Anastasios Kyrillidis, Noah Harding Assistant Professor at the Computer Science department at Rice University. Prior to that, I was a Goldstine PostDoctoral Fellow at IBM T. J. Watson Research Centre (NY), and a Simons Foundation PostDoc member at the University of Texas at Austin. I finished my PhD at the CS Department of EPFL (Switzerland) under the supervision of Volkan Cevher. I got my M.Sc. and Diploma from Electronic and Computer Engineering Dept. at Technical University of Crete (Chania). My research interests include (but not limited to):

  • Optimisation for machine learning
  • Convex and non-convex algorithms and analysis
  • Large-scale optimisation
  • Any problem that includes a math-driven criterion and requires an efficient method for its solution.

Skip to content