ICNCE 2026

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 scheduling...

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 of Boolean...

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 nonlinear...

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 infrastructure....

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 work on...