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

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

Biological spiking neurons with intrinsic dynamics underlie the powerful representation and
learning capabilities of the brain for processing multimodal information in complex …

In recent years, event-driven tactile data learning and event-driven spiking neural network
(SNN) characteristics have provided new methods for tactile event perception. However, it is …

Neuromorphic computing has many opportunities in future autonomous systems, especially
those that will operate at the edge. However, there are relatively few demonstrations of …

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 computers emulate the human brain while being extremely power efficient for
computing tasks. In fact, they are poised to be critical for energy-efficient computing in the …

Neuromorphic computing has several characteristics that make it an extremely compelling
computing paradigm for post Moore computation. Some of these characteristics include …

Neuromorphic computing is a non-von Neumann computing paradigm that performs
computation by emulating the human brain. Neuromorphic systems are extremely energy …

Neuromorphic computing is a non-von Neumann computing paradigm that performs
computation by emulating the human brain. Neuromorphic systems are extremely energy …