Neuromorphic computing technologies will be important for the future of computing, but
much of the work in neuromorphic computing has focused on hardware development. Here …

Microelectronic computers have encountered challenges in meeting all of today's demands
for information processing. Meeting these demands will require the development of …

Photonic systems for high-performance information processing have attracted renewed
interest. Neuromorphic silicon photonics has the potential to integrate processing functions …

There has been recent interest in neuromorphic photonics, a field with the promise to access
pivotal and unexplored regimes of machine intelligence. Progress has been made on …

This paper investigates the role of the materiality of computation in two domains: blockchain
technologies and artificial intelligence (AI). Although historically designed as parallel …

While neuromorphic systems may be the ultimate platform for deploying spiking neural
networks (SNNs), their distributed nature and optimization for specific types of models …

Abstract Machine learning (ML) methods has recently contributed very well in the
advancement of the prediction models used for energy consumption. Such models highly …

Particle accelerators are host to myriad nonlinear and complex physical phenomena. They
often involve a multitude of interacting systems, are subject to tight performance demands …

Smart electronic devices are becoming ubiquitous due to many appealing attributes
including portability, long operational time, rechargeability and compatibility with the user …

Spiking neural networks (SNNs) are largely inspired by biology and neuroscience and
leverage ideas and theories to create fast and efficient learning systems. Spiking neuron …