ICNCE 2026

Abstract: Running AI on edge devices requires high efficiency under strict energy and latency constraints. In-memory and near-memory computing reduce data movement and improve efficiency. However, beyond efficiency, ensuring the trustworthiness of AI systems remains a critical concern. This talk introduces Bayesian electronics, where the intrinsic randomness of emerging nanodevices is used...

Abstract:
Edge AI is entering a new era where functionality is defined not just by efficient inference, but by on‑device learning, continual adaptation, and user personalization. For wearable systems, this shift exposes a fundamental limitation of today’s architectures: energy is no longer spent on computation, but on moving data back and forth across memory hierarchies. As a result,...

Abstract: Various applications demand more and more powerful machine inference in resource-scarce distributed devices. To allow intelligent applications at ultra-low energy and low latency, one needs 1.) custom AI processors, exploiting parallelism and data reuse under strong resource limitations; 2.) efficient ML models, optimized for the target hardware platform; 3.) data-efficient...

Abstract: Many real-life problems — from combinatorial optimization and constraint satisfaction to inference in energy-based AI models — share a common mathematical structure: finding configurations that minimize a complex, non-convex energy landscape. Boolean satisfiability SAT (or MaxSAT), canonical NP-complete (or NP-hard) problems, exemplify this class: the task is to find assignments...

Abstract: A large part of the current effort in AI hardware is directed at accelerating linear operations, especially matrix-vector multiplications. Yet the expressive power of artificial neural networks does not arise from linear operations alone. Neural networks are nonlinear function approximators, and their ability to represent complex input-output relations critically depends on...

Abstract: Modern AI spans an increasingly diverse set of model architectures and computational workflows. Different types of models, such as large language models, multimodal systems, diffusion models, and world models, exhibit distinct computational characteristics. Different workflows such as training, reinforcement learning, and inference also have different demands for compute...

Abstract: Brain-inspired event-based neuromorphic computing is a promising technology for energy-efficient bio-inspired AI. It also enables continuous learning based on local learning algorithms. For maximum energy efficiency, a brain-like in-memory realization is desirable. The Heidelberg BrainScaleS platform is an example of a neuromorphic architecture that combines true in-memory...

Abstract: Language models with recurrent depth, also referred to as universal or looped when considering transformers, are defined by their capacity to increase their computation through the repetition of layers. Recent pretraining efforts have demonstrated that these architectures can scale to modern language modeling tasks while exhibiting advantages in reasoning tasks. This makes their...

Abstract: Oxide-based memristors are emerging as key enabling technologies for neuromorphic hardware and unconventional computing paradigms. These devices can emulate biological synaptic and neuronal functionalities, while also serving as compact computational units for in-memory processing and reservoir computing architectures [1].
In this talk, I will present an overview of our recent...

Abstract: Electronic devices that emulate the excitability of biological cells hold promise for bioelectronic systems capable of detecting, processing, and responding to physiological signals directly at the interface with living tissue[1]. Conventional silicon-based hardware, however, faces challenges in biointegration due to mechanical rigidity, circuit complexity, and relatively high...

Abstract: Polymer-based ECRAMs have been recently developed to the point of demonstrating outstanding performance at the device level. Indeed, all solid-state ECRAMs switch with frequencies exceeding 50MHz even when moderately scaled, they use less than 100fJ per switching event and can be switched billions of times at temperatures up to 90°C. Furthermore, with a judicious choice of...

Abstract: This talk focuses on event-based computing and the broader need to extend current computing architectures toward a new paradigm shift. This shift requires a rethinking of the entire event sensing and processing stack, from sensor design to algorithms and system-level integration.

Recent results in event-based sensing are presented, along with an overview of the key challenges...

Abstract: As artificial intelligence and machine learning technologies are projected to consume increasingly significant amounts of energy, potentially leading to costly, inefficient, and unsustainable systems. Drawing inspiration from the brain’s exceptional energy efficiency, this series of studies seeks to demonstrate that the execution of simple behavioral tasks by biological neuronal...

Abstract: What does it mean when a brainlike system ‘computes’? This is the question of the semantics of neuromorphic computing. In classical digital computing, several mutually connected workouts of computational semantics have matured to textbook standard. These formal frameworks allow one to characterize, analyse and prove, for instance, whether a computer program actually does what...

Abstract: Unconventional sensing and perception: using event-driven technologies for robots
Biological sensory systems have developed to best capture the properties of surrounding objects and environment that are useful for acting in the world. The physical properties of tactile, visual and auditory sensory organs, and the way neurons encode the characteristics of each stimulus allow our...

Abstract: The shift toward edge computing has enabled real-time data processing closer to the source of data collection, reducing latency and improving overall efficiency. Yet this shift imposes strict constraints on power consumption, physical footprint, and computational performance. These constraints cannot be met by conventional hardware approaches alone. At the same time, logic and...

Abstract: Artificial Intelligence (AI) has an energy problem, which is both economically and environmentally unsustainable. The fundamental cause of this problem is the primitive thermodynamic computing principles used in digital processors. The brain offers a fantastic alternative by employing complexity within every neuron, wherein information is processed parallelly by multiple kinetic...