Function approximation by hardware spiking neural network

Farsa, Edris Zaman; Nazari, Soheila; Gholami, Morteza

doi:10.1007/s10825-015-0709-x

Cited by 7 publications

(5 citation statements)

References 27 publications

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“…[13][14][15][16][17][18][19][20][21]33 This part includes digital implementation procedure of the Wilson neuron. [13][14][15][16][17][18][19][20][21]33 This part includes digital implementation procedure of the Wilson neuron.…”

Section: Design and Hardware Implementationmentioning

confidence: 99%

“…Analog implementation of neuro-inspired systems is a logical selection. [13][14][15][16][17][18][19][20][21][37][38][39][40] In the next section, the possibility of hardware implementation of the Wilson model on FPGA is investigated. Some hardware including Blue Brain, 35 Neurogrid, 32 and SpiNNaker, 36 which have specific goals and are developed to implement biological NNs, are really flexible and biologically realistic.…”

Section: Design and Hardware Implementationmentioning

confidence: 99%

“…Field-programmable gate arrays can support an efficient programmable resource for hardware implementation of SNNs. [13][14][15][16][17][18][19][20][21]33 This part includes digital implementation procedure of the Wilson neuron. Analog implementation of neuro-inspired systems is a logical selection.…”

Section: Design and Hardware Implementationmentioning

confidence: 99%

“…It is an essential requirement of the simulation and digital implementation of the large-scale systems that mimic the biologically inspired concepts. [13][14][15][16][17][18][19][20][21] The brain is a complicated system that is composed of a large number of initial building blocks, neurons, which have been thickly interconnected. Generally, a spiking neuron operates like a dynamical system, which can be introduced by a series of differential equations.…”

Section: Introductionmentioning

confidence: 99%

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Digital implementation of biologically inspired Wilson model, population behavior, and learning

Karimi

Gholami

Farsa

2018

Circuit Theory & Apps

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Spiking neurons, as a computational unit, are the main part in biological information processing systems. This paper presents a digital hardware implementation of a biological neuron on a field-programmable gate array due to its high accuracy and high speed, especially for large-scale simulations which is a key objective in the neuromorphic research field. Although this is a computationally expensive task, the use of more biological realistic system results in higher accuracy in mimicking biological behaviors of neural networks. Given that, the Wilson model is one of the most important biological neuron models that can be used in the architecture of spiking neural networks. To be closer to biological systems, a method is proposed to test the possibility of implementation of the Wilson neuron model on digital platforms. The results of the hardware implementation of the Wilson neuron and a spiking network on a field-programmable gate array, capable of character recognition with supervised learning algorithm, are presented in this paper; moreover, population behavior of this model is simulated. In large-scale implementation of 2000 Wilson neuron model, population capability, feasibility, and costs are investigated. This paper presents a method to the implementation of Wilson neurons on digital platforms, suggesting that the available system is an attainable platform for the implementation of large-scale biologically plausible neural networks on field-programmable gate array devices. Hardware synthesis, physical implementation on field-programmable gate array, and theoretical analysis confirm that the proposed model has hardware so that makes it an appropriate model for the large-scale digital implementation. KEYWORDS hardware implementation, learning, spiking neural network, Wilson Model

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Section: Design and Hardware Implementationmentioning

confidence: 99%

Section: Design and Hardware Implementationmentioning

confidence: 99%

Section: Design and Hardware Implementationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Digital implementation of biologically inspired Wilson model, population behavior, and learning

Karimi

Gholami

Farsa

2018

Circuit Theory & Apps

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“…In addition, the leaky integrate and fire model involves differential equations, while the spike response model uses integration computation. The Izhikevich [35][36][37][38][39][40][41][42][43][44][45] and Application Specific Integrated Circuit (ASIC) [5,6,[46][47][48][49][50]. FPGA implementations of SNN have high flexibility and short design time.…”

Section: List Of Tablesmentioning

confidence: 99%

Energy efficient neuromorphic computing circuit and architecture design

Pu¹

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Reconfigurable field‐programmable gate array‐based on‐chip learning neuromorphic digital implementation for nonlinear function approximation

Gholami

Farsa

Karimi

2021

Circuit Theory & Apps

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Hardware implementations of spiking neural networks, which are known as neuromorphic architectures, provide an explicit understanding of brain performance. As a result, biological features of the brain may well inspire the next generation of computers and electronic systems used in such areas as signal processing, image processing, function approximation, and pattern recognition. Approximating nonlinear functions has many uses in computer science and applied mathematics. The sigmoid is the most universal activation function in neural networks by which the relationship between biological and artificial neurons is defined. It is a suitable option for predicting the probability of anything from 0 to 1 as output. In this paper, a spiking neural network using Izhikevich neurons and a gradient descent learning algorithm are propounded to approximate the sigmoid and other nonlinear functions. The flexibility of the spiking network is demonstrated by showing the average relative errors in the approximation process. A time-and cost-efficient digital neuromorphic implementation on the base of on-chip learning method for approximating the sigmoid function is also discussed. The paper reports the results of the hardware synthesis and the spiking network's physical implementation on a field-programmable gate array. The maximum frequency and throughput of the implemented network were 83.209 MHz and 9.86 Mb/s, respectively. K E Y W O R D Sdigital hardware, field-programmable gate array (FPGA), hardware implementation, neuromorphic, on-chip learning, spiking neural networks (SNNs)

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Function approximation by hardware spiking neural network

Cited by 7 publications

References 27 publications

Digital implementation of biologically inspired Wilson model, population behavior, and learning

Digital implementation of biologically inspired Wilson model, population behavior, and learning

Energy efficient neuromorphic computing circuit and architecture design

Reconfigurable field‐programmable gate array‐based on‐chip learning neuromorphic digital implementation for nonlinear function approximation

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