Neuromorphic computing covers a diverse range of approaches to information processing
all of which demonstrate some degree of neurobiological inspiration that differentiates them …

Multiple neuromorphic systems use spiking neural networks (SNNs) to perform computation
in a way that is inspired by concepts learned about the human brain. SNNs are artificial …

Since the beginning of information processing by electronic components, the nervous
system has served as a metaphor for the organization of computational primitives. Brain …

Neuromorphic computing systems comprise networks of neurons that use asynchronous
events for both computation and communication. This type of representation offers several …

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 …

This paper proposes a neuromorphic platform for on-FPGA online spike timing dependant
plasticity (STDP) learning, based on the COordinate Rotation DIgital Computer (CORDIC) …

Solid-state neuromorphic systems based on transistors or memristors have yet to achieve
the interconnectivity, performance, and energy efficiency of the brain due to excessive noise …

While digital integrated circuits with von Neumann architectures, having exponentially
evolved for half a century, are an indispensable building block of today's information society …

A switched-capacitor (SC) neuromorphic system for closed-loop neural coupling in 28 nm
CMOS is presented, occupying 600 um by 600 um. It offers 128 input channels (ie …

To achieve the artificial general intelligence (AGI), imitate the intelligence? or imitate the
brain? This is the question! Most artificial intelligence (AI) approaches set the understanding …