Though neuromorphic computers have typically targeted applications in machine learning
and neuroscience ('cognitive'applications), they have many computational characteristics …

Neuromorphic computing, which aims to replicate the computational structure and
architecture of the brain in synthetic hardware, has typically focused on artificial intelligence …

Current neuromorphic systems often may be difficult to use and costly to deploy. There exists
a need for a simple yet flexible neuromorphic development platform which can allow …

Neuromorphic computing is poised to become a promising computing paradigm in the post
Moore's law era due to its extremely low power usage and inherent parallelism. Traditionally …

Software simulators play a critical role in the development of new algorithms and system
architectures in any field of engineering. Neuromorphic computing, which has shown …

Graph convolutional networks have risen in popularity in recent years to tackle problems that
are naturally represented as graphs. However, real-world graphs are often sparse, which …

The Abisko project aims to develop an energy-efficient spiking neural network (SNN)
computing architecture and software system capable of autonomous learning and operation …

Neuromorphic computing is a critical future technology for the computing industry, but it has
yet to achieve its promise and has struggled to establish a cohesive research community. A …

Modeling epidemic spread with spike-based models Page 1 Modeling epidemic spread with
spike-based models Kathleen Hamilton Oak Ridge National Laboratory Oak Ridge …

Novel uses of graphical processing units for accelerated computation revolutionized the field
of high-performance scientific computing by providing specialized workflows tailored to …