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

Brain-inspired computing uses insights from neuroscience to develop more efficient
computing systems. The approach is of use in a broad range of applications—from neural …

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 …

In this work, we address the problem of training a neuromorphic agent to work on data from
event-based cameras. Although event-based camera data is much sparser than standard …

Increasing power costs for large-scale computing in a post-Moores Law system have forced
the high-performance computing community to explore heterogeneous systems …

The widely parallel, spiking neural networks of neuromorphic processors can enable
computationally powerful formulations. While recent interest has focused on primarily …

Spiking neuromorphic computers (SNCs) are promising as a post Moore's law technology
partly because of their potential for very low power computation. SNCs have primarily been …

Future high-performance computing (HPC) platforms increasingly depend on
heterogeneous node architectures to meet power and performance requirements. While …

With the advent of large-scale neuromorphic platforms, we seek to better understand the
applications of neuromorphic computing to more general-purpose computing domains …

Neuromorphic hardware architectures represent a growing family of potential post-Moore's
Law Era platforms. Largely due to event-driving processing inspired by the human brain …