The usefulness of an artificial analog neural network is closely bound to its trainability. This paper introduces a new analog neural network architecture using weights determined...
Johannes Schemmel, Karlheinz Meier, Felix Schü...
Abstract. Finite-state machines are the most pervasive models of computation, not only in theoretical computer science, but also in all of its applications to real-life problems, a...
In this paper we analyze the complexity of scheduling wireless links in the physical interference model with analog network coding capability. We study two models with different d...
- This paper aims at discussing the implementation of simulation systems for SNN based on analog computation cores (neuromimetic ICs). Such systems are an alternative to completely...
Sylvie Renaud, Jean Tomas, Yannick Bornat, Adel Da...
We develop a neural network that learns to separate the nominal from the faulty instances of a circuit in a measurement space. We demonstrate that the required separation boundari...