Opportunities for neuromorphic computing algorithms and applications
Neuromorphic computing technologies will be important for the future of computing, but
much of the work in neuromorphic computing has focused on hardware development. Here …
much of the work in neuromorphic computing has focused on hardware development. Here …
A review of non-cognitive applications for neuromorphic computing
Though neuromorphic computers have typically targeted applications in machine learning
and neuroscience ('cognitive'applications), they have many computational characteristics …
and neuroscience ('cognitive'applications), they have many computational characteristics …
Advancing neuromorphic computing with loihi: A survey of results and outlook
Deep artificial neural networks apply principles of the brain's information processing that led
to breakthroughs in machine learning spanning many problem domains. Neuromorphic …
to breakthroughs in machine learning spanning many problem domains. Neuromorphic …
Neuromorphic scaling advantages for energy-efficient random walk computations
Neuromorphic computing, which aims to replicate the computational structure and
architecture of the brain in synthetic hardware, has typically focused on artificial intelligence …
architecture of the brain in synthetic hardware, has typically focused on artificial intelligence …
[HTML][HTML] Impact of quantum and neuromorphic computing on biomolecular simulations: Current status and perspectives
New high-performance computing architectures are becoming operative, in addition to
exascale computers. Quantum computers (QC) solve optimization problems with …
exascale computers. Quantum computers (QC) solve optimization problems with …
NeuroXplorer 1.0: An extensible framework for architectural exploration with spiking neural networks
Recently, both industry and academia have proposed many different neuromorphic
architectures to execute applications that are designed with Spiking Neural Network (SNN) …
architectures to execute applications that are designed with Spiking Neural Network (SNN) …
Spiking neuromorphic networks for binary tasks
In this paper, we focus on the hand construction of small-scale, spiking, neuromorphic
networks. They are partitioned into two sets. The first set performs the binary operations …
networks. They are partitioned into two sets. The first set performs the binary operations …
Computational complexity of neuromorphic algorithms
Neuromorphic computing has several characteristics that make it an extremely compelling
computing paradigm for post Moore computation. Some of these characteristics include …
computing paradigm for post Moore computation. Some of these characteristics include …
Neuromorphic computing is Turing-complete
Neuromorphic computing is a non-von Neumann computing paradigm that performs
computation by emulating the human brain. Neuromorphic systems are extremely energy …
computation by emulating the human brain. Neuromorphic systems are extremely energy …
A spiking neural network mimics the oculomotor system to control a biomimetic robotic head without learning on a neuromorphic hardware
Facilitated by the emergence of neuromorphic hardware, neuromorphic algorithms mimic
the brain's asynchronous computation to improve energy efficiency, low latency, and …
the brain's asynchronous computation to improve energy efficiency, low latency, and …