LSI implementation of pulse-output neural network with programmable synapse

Sato, Shusei; Yumine, Manabu; Yama, Takayuki; Murota, Junichi; Nakajima, Koji; Sawada, Yasuji

doi:10.1109/ijcnn.1992.287140

Cited by 5 publications

(2 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The probability of a single bit in a given state is used to encode the desired signal, thus reducing the area cost since only one wire is needed to carry the whole numerical value with this probabilistic codification. Hence, complex functions whose implementation requires a high amount of area, such as multiplications [38], are performed by using a single logic gate, with great savings in terms of area and power [39,40]. However, this area reduction has a cost in terms of precision loss.…”

Section: Artificial Neural Network Applied To Virtual Screeningmentioning

confidence: 99%

Using Stochastic Computing for Virtual Screening Acceleration

et al. 2021

View full text Add to dashboard Cite

Stochastic computing is an emerging scientific field pushed by the need for developing high-performance artificial intelligence systems in hardware to quickly solve complex data processing problems. This is the case of virtual screening, a computational task aimed at searching across huge molecular databases for new drug leads. In this work, we show a classification framework in which molecules are described by an energy-based vector. This vector is then processed by an ultra-fast artificial neural network implemented through FPGA by using stochastic computing techniques. Compared to other previously published virtual screening methods, this proposal provides similar or higher accuracy, while it improves processing speed by about two or three orders of magnitude.

show abstract

Section: Artificial Neural Network Applied To Virtual Screeningmentioning

confidence: 99%

Using Stochastic Computing for Virtual Screening Acceleration

et al. 2021

View full text Add to dashboard Cite

show abstract

“…A special case of stochastic neural networks, Boltzmann machines, have also been popular in neuromorphic systems. The general Boltzmann machine was utilized in neuromorphic systems primarily in the early 1990's [12], [1193]- [1199], but it has seen occasional implementations in more recent publications [1200]- [1203]. A more common use of the Boltzmann model is the restricted Boltzmann machine, because the training time is significantly reduced when compared with a general Boltzmann machine.…”

Section: Network Modelsmentioning

confidence: 99%

A Survey of Neuromorphic Computing and Neural Networks in Hardware

Schuman¹,

Potok²,

Patton³

et al. 2017

Preprint

176

180

View full text Add to dashboard Cite

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 biologically inspired approach has created highly connected synthetic neurons and synapses that can be used to model neuroscience theories as well as solve challenging machine learning problems. The promise of the technology is to create a brainlike ability to learn and adapt, but the technical challenges are significant, starting with an accurate neuroscience model of how the brain works, to finding materials and engineering breakthroughs to build devices to support these models, to creating a programming framework so the systems can learn, to creating applications with brain-like capabilities. In this work, we provide a comprehensive survey of the research and motivations for neuromorphic computing over its history. We begin with a 35-year review of the motivations and drivers of neuromorphic computing, then look at the major research areas of the field, which we define as neuro-inspired models, algorithms and learning approaches, hardware and devices, supporting systems, and finally applications. We conclude with a broad discussion on the major research topics that need to be addressed in the coming years to see the promise of neuromorphic computing fulfilled. The goals of this work are to provide an exhaustive review of the research conducted in neuromorphic computing since the inception of the term, and to motivate further work by illuminating gaps in the field where new research is needed.

show abstract