[HTML][HTML] Impact of quantum and neuromorphic computing on biomolecular simulations: Current status and perspectives
S Diaz-Pier, P Carloni - Current Opinion in Structural Biology, 2024 - Elsevier
New high-performance computing architectures are becoming operative, in addition to
exascale computers. Quantum computers (QC) solve optimization problems with …
exascale computers. Quantum computers (QC) solve optimization problems with …
Mosaic: in-memory computing and routing for small-world spike-based neuromorphic systems
The brain's connectivity is locally dense and globally sparse, forming a small-world graph—
a principle prevalent in the evolution of various species, suggesting a universal solution for …
a principle prevalent in the evolution of various species, suggesting a universal solution for …
Integration of neuromorphic AI in event-driven distributed digitized systems: Concepts and research directions
Increasing complexity and data-generation rates in cyber-physical systems and the
industrial Internet of things are calling for a corresponding increase in AI capabilities at the …
industrial Internet of things are calling for a corresponding increase in AI capabilities at the …
On-sensor data filtering using neuromorphic computing for high energy physics experiments
This work describes the investigation of neuromorphic computing-based spiking neural
network (SNN) models used to filter data from sensor electronics in high energy physics …
network (SNN) models used to filter data from sensor electronics in high energy physics …
Spiking NeRF: Representing the Real-World Geometry by a Discontinuous Representation
A crucial reason for the success of existing NeRF-based methods is to build a neural density
field for the geometry representation via multiple perceptron layers (MLPs). MLPs are …
field for the geometry representation via multiple perceptron layers (MLPs). MLPs are …
Abisko: Deep codesign of an architecture for spiking neural networks using novel neuromorphic materials
The Abisko project aims to develop an energy-efficient spiking neural network (SNN)
computing architecture and software system capable of autonomous learning and operation …
computing architecture and software system capable of autonomous learning and operation …
Progress in End-to-End Optimization of Detectors for Fundamental Physics with Differentiable Programming
M Aehle, L Arsini, RB Barreiro, A Belias, F Bury… - arXiv preprint arXiv …, 2023 - arxiv.org
In this article we examine recent developments in the research area concerning the creation
of end-to-end models for the complete optimization of measuring instruments. The models …
of end-to-end models for the complete optimization of measuring instruments. The models …
From Oxides to 2D Materials: Advancing Memristor Technologies for Energy-Efficient Neuromorphic Computing
This review presents a comparative analysis of the analog switching performance of oxide-
and two-dimensional (2D)-material-based memristors, focusing on their application in …
and two-dimensional (2D)-material-based memristors, focusing on their application in …
Bioinspired smooth neuromorphic control for robotic arms
Beyond providing accurate movements, achieving smooth motion trajectories is a long-
standing goal of robotics control theory for arms aiming to replicate natural human …
standing goal of robotics control theory for arms aiming to replicate natural human …
Performance and energy simulation of spiking neuromorphic architectures for fast exploration
Recent work in neuromorphic computing has proposed a range of new architectures for
Spiking Neural Network (SNN)-based systems. However, neuromorphic design lacks a …
Spiking Neural Network (SNN)-based systems. However, neuromorphic design lacks a …