A VLSI Systolic Array Dedicated to Hopfield Neural Network

Blayo, François; Hurat, Philippe

doi:10.1007/978-1-4613-1619-0_24

Cited by 21 publications

(5 citation statements)

References 2 publications

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“…Winner-take-all networks, which utilize recurrent inhibitory connections to force a single output, have also been implemented in neuromorphic systems [1107]- [1109]. Hopfield networks were especially common in earlier neuromorphic implementations, as is consistent with neural network research at that time [5], [6], [13], [15], [759], [764], [813], [1110]- [1148], but there are also more recent implementations [838], [1149]- [1159]. Similarly, associative memory based implementations were also significantly more popular in earlier neuromorphic implementations [1053], [1160]- [1182].…”

Section: Network Modelsmentioning

confidence: 83%

“…Full custom or application specific integrated circuit (ASIC) chips have also been very common for neuromorphic implementations [6], [9], [348], [350], [389]- [404], [438], [477], [677], [683], [749]- [764], [764]- [808], [1047], [1048], [1054]- [1060], [1100], [1101], [1118]- [1130], [1170], [1183]- [1185], [1204], [1205], [1212], [1236]- [1242], [1260], [1275], [1283], [1363], [1410], [1527]- [1561]. IBM's TrueNorth, one of the most popular present-day neuromorphic implementations, is a full custom ASIC design [1562]- [1569].…”

Section: A High-levelmentioning

confidence: 99%

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A Survey of Neuromorphic Computing and Neural Networks in Hardware

Schuman¹,

Potok²,

Patton³

et al. 2017

Preprint

176

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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Section: Network Modelsmentioning

confidence: 83%

Section: A High-levelmentioning

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

show abstract

“…The first one for FPGA, which has been used frequently in neuromorphic systems [17]. Another one is custom or application-specific integrated circuit (ASIC) chips, which is also common neuromorphic implementations [35].…”

Section: Introductionmentioning

confidence: 99%

An Overview of Neuromorphic Computing for Artificial Intelligence Enabled Hardware-Based Hopfield Neural Network

Abdulghani

Zahid

et al. 2020

IEEE Access

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Compared with von Neumann's computer architecture, neuromorphic systems offer more unique and novel solutions to the artificial intelligence discipline. Inspired by biology, this novel system has implemented the theory of human brain modeling by connecting feigned neurons and synapses to reveal the new neuroscience concepts. Many researchers have vastly invested in neuro-inspired models, algorithms, learning approaches, operation systems for the exploration of the neuromorphic system and have implemented many corresponding applications. Recently, some researchers have demonstrated the capabilities of Hopfield algorithms in some large-scale notable hardware projects and seen significant progression. This paper presents a comprehensive review and focuses extensively on the Hopfield algorithm's model and its potential advancement in new research applications. Towards the end, we conclude with a broad discussion and a viable plan for the latest application prospects to facilitate developers with a better understanding of the aforementioned model in accordance to build their own artificial intelligence projects. Neuromorphic computing, neuro-inspired model, Hopfield algorithm, artificial intelligence. INDEX TERMS

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“…Finally, for neurochips and neurocomputers which are dedicated to a certain neural architecture (e.g., the Boltzmann machine (Murray et al 1992(Murray et al , 1994; Kohonen's self-organizing feature maps (Hochet et al C1.4, C2.1.1 1991, Goser et al 1989, Rüping and Rückert 1996, Tryba et al 1990, Thiran 1993, Thiran et al 1994, Thole et al 1993; Hopfield networks (Blayo and Hurat 1989, Gascuel et al 1992, Graf and de Vegvar C1.3.4 1987a, b, Graf et al 1987, Savran and Morgül 1991, Sivilotti et al 1986, Weinfeld 1989, Yasunaga et al 1989; Neocognitron (Trotin andDarbel 1993, White andElmasry 1992); radial basis functions and C2.1.3, C1.6.2 restricted coulomb energy (LeBouquin 1994, Scofield and Reilly 1991)), or for those which are built as C1.6.3.1 stochastic devices (Clarkson and Ng 1993, Clarkson et al 1993a, b, Köllmann et al 1966, it is almost C1.4 impossible to assess their speed. It should be mentioned that due to such unsurmountable problems there is usually little if any information on benchmarks.…”

Section: Beiu Et Almentioning

confidence: 99%

“…Although the system performs lower than CNAPS (described below), we have to mention that SNAP uses 32-bit floating point arithmetic. • The APLYSIE chip is a two-dimensional systolic array dedicated for Hopfield-type networks (Blayo and Hurat 1989). Since the outputs are only +1 and −1, the synaptic multiplication can be performed by an adder/subtracter (like in Weinfeld's 1989 solution).…”

mentioning

confidence: 99%

Digital integrated circuit implementations

Beiu¹

Handbook of Neural Computation

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This section considers some of the alternative approaches towards modeling biological functions by digital circuits. It starts by introducing some circuit complexity issues and arguing that there is considerable computational and physiological justification that shallow threshold gate circuits are computationally more efficient than classical Boolean circuits. We comment on the tradeoff between the depth and the size of a threshold gate circuit, and on how design parameters like fan-in, weights and thresholds influence the overall area and time performances of a digital neural chip. This is followed by briefly discussing the constraints imposed by digital technologies and by detailing several possible classification schemes as well as the performance evaluation of such neurochips and neurocomputers. Lastly, we present many typical and recent examples of implementation and mention the 'VLSI-friendly learning algorithms' as a promising direction of research.

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A VLSI Systolic Array Dedicated to Hopfield Neural Network

Cited by 21 publications

References 2 publications

A Survey of Neuromorphic Computing and Neural Networks in Hardware

A Survey of Neuromorphic Computing and Neural Networks in Hardware

An Overview of Neuromorphic Computing for Artificial Intelligence Enabled Hardware-Based Hopfield Neural Network

Digital integrated circuit implementations

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