Neuromorphic computing is becoming a popular approach for implementations of brain-
inspired machine learning tasks. As a paradigm for both hardware and algorithm design …

Neuromorphic Computing, a concept pioneered in the late 1980s, is receiving a lot of
attention lately due to its promise of reducing the computational energy, latency, as well as …

Guided by brain-like 'spiking'computational frameworks, neuromorphic computing—brain-
inspired computing for machine intelligence—promises to realize artificial intelligence while …

While Moore's law has driven exponential computing power expectations, its nearing end
calls for new avenues for improving the overall system performance. One of these avenues …

Neuromorphic computing has come to refer to a variety of brain-inspired computers, devices,
and models that contrast the pervasive von Neumann computer architecture. This …

Neuromorphic engineering (NE) encompasses a diverse range of approaches to information
processing that are inspired by neurobiological systems, and this feature distinguishes …

On the road toward artificial general intelligence (AGI), two solution paths have been
explored: neuroscience-driven neuromorphic computing such as spiking neural networks …

The term “neuromorphic” was originally introduced by Mead in the late 1980s, 1 referring to
devices and systems that imitated certain elements of biological neural systems. However …

Neuromorphic computing is an emerging research field that aims to develop new intelligent
systems by integrating theories and technologies from multiple disciplines, such as …

Neuromorphic Engineering has emerged as an exciting research area, primarily owing to
the paradigm shift from conventional computing architectures to data-driven, cognitive …