Current neural networks are accumulating accolades for their performance on a variety of
real-world computational tasks including recognition, classification, regression, and …

A novel micro-kernel approach to building parallel/distributed simulation systems is
presented. Using this approach, a unified system architecture is developed for incorporating …

Deep Neural Networks (DNNs) have proven very effective for classification and generative
tasks, and are widely adapted in a variety of fields including vision, robotics, speech …

Neuromorphic computing is a broad category of non–von Neumann architectures that mimic
biological nervous systems using hardware. Current research shows that this class of …

Lately, important large-scale simulation applications, such as emergency/event planning
and response, are emerging that are based on discrete event models. The applications are …

Research in parallel simulation has been around for more than two decades. However, the
number of papers reporting on its application to real world problems is limited. At the 2002 …

Automatic parallelization of models has been the" Holy Grail" of the PDES community for the
last 20 years. In this paper we present LORAIN--Low Overhead Runtime Assisted Instruction …

Spike neural networks are inspired by animal brains, and outperform traditional neural
networks on complicated tasks. However, spike neural networks are usually used on a large …

Agent-based modelling and simulation is a promising methodology that can be applied in
the study of population dynamics. The main advantage of this technique is that it allows …

A cortical simulator optimizing the simulation scale and time through computationally
efficient simulation of neurons in a clock-driven and synapses in an event-driven fashion …