Deep Learning meets Neuromorphic Hardware

The goal of this interdisciplinary workshop is to bring together researchers from different fields such as Computer Science, Engineering, Physics, Materials Science, Control Theory, and Dynamical Systems to share and discuss new exciting ideas at the intersection of deep learning and neuromorphic computing.

Workshop structure

The workshop will be structured in two tracks.

The first track will focus on deep learning concepts for neuromorphic implementations, including reservoir computing, light-weight neural networks, semi-randomized neural networks, partially untrained structured networks, continuous-time recurrent neural networks, neural ODEs, spiking neural networks, unconventional computing, supervised training algorithms beyond backpropagation, and unsupervised training algorithms for neural networks.

The second track will focus on neuromorphic hardware for deep learning, including electronic, mechanical, and photonic neuromorphic hardware, in-memory and analogue computing architectures, in-materia computing architectures, hardware integration of neural networks, hardware implementation of neuronal and synaptic functions, massively parallel hardware networks, and quantum computing.

We welcome submissions from researchers working in a variety of fields, including but not limited to Computer Science, Engineering, Physics, Materials Science, Control Theory, and Dynamical Systems. Submissions should clearly demonstrate the relevance to the intersection of deep learning and neuromorphic computing, and highlight the potential impact of the proposed research on the development of energy-efficient intelligent systems.

All submissions will be reviewed by the program committee, and accepted papers will be presented at the workshop. The workshop will feature keynote talks by leading experts in the field, as well as opportunities for discussion and collaboration.

Submission

Papers should be written in English and formatted according to the Springer LNCS format. Please, use this template for preparing your submission.

We invite both abstracts (up to 4 pages, references excluded) and full papers (up to 14 pages, references excluded). Submissions should be made through the workshop's CMT submission page. 

Papers authors will have the faculty to opt-in or opt-out for publication of their submitted papers in the joint post-workshop proceedings published by Springer Communications in Computer and Information Science, organised by focused scope and possibly indexed by WOS. 

Notice that novelty is not essential for contributions that will not appear in the workshop proceedings (presentation-only contributions), as we invite abstracts and papers that have already been presented or published elsewhere with the aim of maximizing the dissemination and cross-pollination of ideas among the deep learning and neuromorphic hardware communities.

At least one author of each accepted paper must have a full registration and be in-person at the conference to present the paper. Papers without a full registration or in-presence presentation won't be included in the post-workshop Springer proceedings.

Link for submissions: https://cmt3.research.microsoft.com/ECMLPKDDworkshop2023/Submission/Index
After logging in, create a new submission in your author console, and select the track on "Deep learning meets Neuromorphic Hardware".

Please note that there is a section in the submission form where you can indicate whether your submission is for presentation-only or not.

For any information, don't hesitate to contact the workshop organizers at deeplearningneuromorphic_ecml23@googlegroups.com