… the published works in neuromorphic computing and hardware implementations of neural
networks and to discuss open issues for the future of neuromorphic computing. The remainder …

… Neuromorphic computing takes inspiration … neuromorphic computing. We review striking
results that leverage physics to enhance the computing capabilities of artificial neural networks, …

… To that effect, researchers have designed neuromorphic … While most large-scale neuromorphic
systems have been … that neuromorphic computing based on spiking neural networks (…

… neuromorphic computing substrate and use it for emulating several types of neural networks…
been explicitly designed as a universal neural network emulator, are its inherent parallelism …

… neuromorphic research processor designed to support a broad range of spiking neural
networks … -of-the-art contemporary computing architectures. This survey reviews results that are …

… Computational precision and repeatability are really the least of a neuron’s concerns.
Instead, biological neural networks excel in performing massive high-speed computations in …

… Neuromorphic computing could address the inherent limitations of conventional silicon …
Recent work on silicon-based asynchronous spiking neural networks and large crossbar …

… computing systems, including artificial neural networks that run on general purpose hardware
and neuromorphic networks … implementations of quantum neural networks. We divide them …

… neuromorphic computing systems. We first review recent advances in the application of NVM
devices to three computing paradigms: spiking neural networks (… neuromorphic computing …

… a neuron-for-neuron basis, the most efficient substrates for neural network operation today
are dedicated neuromorphic … To achieve high efficiency, neuromorphic architectures can use …