Paper submission deadline: 5 June 2026
Notification of acceptance: 1 July 2026
The intersection between deep learning and neuromorphic computing presents a promising avenue for the development of energy-efficient and adaptive intelligent systems. While deep learning models have achieved remarkable performance across a wide range of applications, their scalability and sustainability are increasingly limited by the computational and energetic costs of training and deployment. Neuromorphic computing, inspired by biological neural systems, offers an alternative perspective in which computation, memory, and learning emerge from the dynamics of physical substrates rather than from purely digital abstractions.
The potential of this intersection lies in its ability to deploy the power of deep learning on intelligent devices that require energy efficiency for working autonomously, enabling a truly pervasive AI. The advantages of neuromorphic hardware over classical digital computing architectures include faster speed, lower power consumption, higher integration density, analog computing, and larger data throughput. This makes neuromorphic hardware an attractive alternative for implementing deep learning models in real-world applications. Importantly, this intersection is not limited to the efficient implementation of established learning models. Constraints imposed by neuromorphic substrates often encourage alternative representations, distributed learning mechanisms, and dynamical modes of computation that challenge conventional optimization-centric views of deep learning, opening new directions in learning theory and model design.
We invite submissions of original research papers, position papers, and extended abstracts that converge at the intersection of deep learning and neuromorphic computing. We solicit contributions not only focusing on technical aspects, but also considering broader ethical and societal implications of energy-efficiency in AI.
Topics of interest include, but are not limited to:
Deep learning concepts for neuromorphic implementations:
Reservoir computing, light-weight and semi-randomized neural networks
Continuous-time recurrent neural networks and Neural ODEs
Spiking neural networks
Unconventional computing
Advanced Training Algorithms beyond Backpropagation
Reinforcement Learning in Neuromorphic Systems
Interoperability and Compatibility Challenges
Scalability of hardware-friendly Neural Networks design
Ethical and societal implications of energy-efficient AI systems
Neuromorphic hardware for deep learning:
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
Quantum computing
Physical computing
Integration and Scalability of Neural Networks in Hardware
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.
Papers should be written in English and formatted according to the Springer LNCS format.
Full papers should be no more than 16 pages in length (including references), while position papers and extended abstracts should be no more than 6 pages in length (including references).
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.