VLSI neural network with digital weights and analog multipliers

Koosh, V.F.; Goodman, R.M.

doi:10.1109/iscas.2001.921290

Cited by 12 publications

(10 citation statements)

References 9 publications

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“…A chip implementing the above circuits was fabricated in a 1.2-m CMOS process [11]. All synapse and neuron transistors were 3.6 m/3.6 m to keep the layout small.…”

Section: E Test Resultsmentioning

confidence: 99%

Analog VLSI neural network with digital perturbative learning

Koosh

Goodman

2002

IEEE Trans. Circuits Syst. II

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Abstract-Two feed-forward neural-network hardware implementations are presented. The first uses analog synapses and neurons with a digital serial weight bus. The chip is trained in loop with the computer performing control and weight updates. By training with the chip in the loop, it is possible to learn around circuit offsets. The second neural network also uses a computer for the global control operations, but all of the local operations are performed on chip. The weights are implemented digitally, and counters are used to adjust them. A parallel perturbative weight update algorithm is used. The chip uses multiple, locally generated, pseudorandom bit streams to perturb all of the weights in parallel. If the perturbation causes the error function to decrease, the weight change is kept; otherwise, it is discarded. Test results from a very large scale integration (VLSI) prototype are shown of both networks successfully learning digital functions such as AND and XOR.Index Terms-Analog very large scale integration (VLSI), chip-in-loop training algorithm, learning, neural chips, neural network, neuromorphic, perturbation techniques, VLSI feed-forward neural network.

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“…A chip implementing the above circuits was fabricated in a 1.2-m CMOS process [11]. All synapse and neuron transistors were 3.6 m/3.6 m to keep the layout small.…”

Section: E Test Resultsmentioning

confidence: 99%

Analog VLSI neural network with digital perturbative learning

Koosh

Goodman

2002

IEEE Trans. Circuits Syst. II

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“…Other approaches for on-chip supervised weight training have been utilized. These approaches include the least-mean-squares algorithm [750], [787], [1025], [1026], weight perturbation [19], [625], [655], [669], [682], [698], [699], [708], [710], [712], [713], [715], [736], [834], [835], [841], [845]- [847], [856], [1078]- [1080], [1098], [1099], [1148], [1304], training specifically for convolutional neural networks [1305], [1306] and others [169], [220], [465], [714], [804], [864], [865], [1029], [1049], [1307]- [1320]. Other on-chip supervised learning mechanisms are built for particular model types, such as Boltzmann machines, restricted Boltzmann machines, or deep belief networks [12], [627], [1135], [1193]<...>…”

Section: A Supervised Learningmentioning

confidence: 99%

A Survey of Neuromorphic Computing and Neural Networks in Hardware

Schuman¹,

Potok²,

Patton³

et al. 2017

Preprint

173

180

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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.

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“…In addition, his simple synapses involve single transistors. Noteworthy neurons capable of learning have been proposed [35,[41][42][43][44][45][46][47]. Koosh and Goodman [42] put a digital computer in the loop for training, control and weight updates, and the neural network is entirely analog, a style realized by several research groups.…”

Section: Neural Modelingmentioning

confidence: 99%