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 …

Recently, both industry and academia have proposed several different neuromorphic
systems to execute machine learning applications that are designed using Spiking Neural …

The brain is the perfect place to look for inspiration to develop more efficient neural
networks. The inner workings of our synapses and neurons provide a glimpse at what the …

Deep artificial neural networks apply principles of the brain's information processing that led
to breakthroughs in machine learning spanning many problem domains. Neuromorphic …

The explosion of “big data” applications imposes severe challenges of speed and scalability
on traditional computer systems. As the performance of traditional Von Neumann machines …

CMOS-RRAM integration holds great promise for low energy and high throughput
neuromorphic computing. However, most RRAM technologies relying on filamentary …

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 …

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 …

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 …

Neuromorphic computing offers the opportunity to implement extremely low power artificial
intelligence at the edge. Control applications, such as autonomous vehicles and robotics …