A Novel Nonlinear Function Evaluation Approach for Efficient FPGA Mapping of Neuron and Synaptic Plasticity Models

Jokar, Ehsan; Abolfathi, Hadis; Ahmadi, Majid

doi:10.1109/tbcas.2019.2900943

Cited by 18 publications

(7 citation statements)

References 56 publications

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“…The precise studying of the brain and understanding the possibilities of connecting nerve cells with the machine is a very important issue in neuroscience. [28][29][30][31][32][33][34][35][36][37][38][39][40] By implementing the large-scale neural networks, containing a large number of proposed neuronal models, a real central nervous system will be achieved.…”

Section: Discussionmentioning

confidence: 99%

“…The goal of this paper was primarily help to provide a scalable platform allowing the study of neurological diseases. The precise studying of the brain and understanding the possibilities of connecting nerve cells with the machine is a very important issue in neuroscience 28–40 . By implementing the large‐scale neural networks, containing a large number of proposed neuronal models, a real central nervous system will be achieved.…”

Section: Discussionmentioning

confidence: 99%

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Multiplierless low‐cost implementation of Hindmarsh–Rose neuron model in case of large‐scale realization

Haghiri

Yahya

Rezaei

et al. 2023

Circuit Theory & Apps

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Implementation of neural networks in case of hardware helps us to understand the different parts of the human brain operation, using artificial intelligence (AI). This paper presents a new model of the Hindmarsh-Rose (HR) Neuron that is based on basic polynomial functions called Nyquist-look up table-Hindmarsh-Rose (N-LUT-HR) based on an accurate sampling of the original model. The proposed approach is investigated in terms of its digital realization feasibility. According to high matching between the original and proposed terms, it is showed that the new modified model can follow all spiking patterns of primary model with low-error computations. In hardware case, the proposed and original models are implemented on Xilinx FPGA XC2VP30 chip to validate different aspects of the simulation results. Hardware results demonstrate that our model regenerates the desired patterns in low-cost and high-frequency (speed-up) in comparison with the other similar works. Overall saving in FPGA resources show that this new model is capable of being used in large-scale networks in case of minimum required resources (FPGA costs). In addition, the analysis of hardware indicates that the new circuits can work in a maximum frequency of 123 MHz with 98.25% saving in FPGA costs (resources utilization of FPGA).

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Multiplierless low‐cost implementation of Hindmarsh–Rose neuron model in case of large‐scale realization

Haghiri

Yahya

Rezaei

et al. 2023

Circuit Theory & Apps

Self Cite

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show abstract

“…By developing a nonlinear function evaluation approach based on the effective uniform PWL segmentation method, Jokar et al obtained a new approximation of nonlinear terms in the neuron model. They implemented the proposed approaches of HR, FHN, and IZ neuron models on an FPGA (Jokar et al 2019). Malik et al developed a fractional-order HR neuron model.…”

Section: Introductionmentioning

confidence: 99%

A Raspberry Pi Based Hardware Implementations of Various Neuron Models

Yucedag

Dalkiran

2022

Preprint

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The implementation of biological neuron models plays an important role to understand brain functionality and robotic applications. Analog and digital methods are preferred during implementation processes. The Raspberry Pi (RPi) microcontroller/microprocessor has the potential to be a new platform that can easily solve complex mathematical operations, does not have memory limitations, which will take advantage while realizing biological neuron models. In this paper, Hodgkin-Huxley (HH), FitzHugh-Nagumo (FHN), Morris-Lecar (ML), Hindmarsh-Rose (HR), and Izhikevich (IZ) neuron models, which are the most popular in the literature, have been both implemented on a standard equipped RPi and simulated on MATLAB. For the numerical solution of each neuron model, the one-step method (4th Runge-Kutta (RK4), the new version of Runge-Kutta (RKN)), the multi-step method (Adams-Bashforth (AB), Adams-Moulton (AM)), and predictor-corrector method (Adams-Bashforth-Moulton (ABM)) are preferred to compare results. The implementation of HH, ML, FHN, HR, and IZ neuron models on RPi and the comparison of RK4, RKN, AB, AM and ABM numerical methods in the implementation of neuron models were made for the first time in this study. Firstly, MATLAB simulations of the various behaviours which belong to HH, ML, FHN, HR, and IZ neuron models were completed. Then those models were realized on RPi and the outputs of the models are experimentally produced. The error values between the simulation and implementation results were calculated and also presented in the tables. The experimental results show that RPi can be considered as a new tool to realize complex neuron models.

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“…Previous works, from single neurons to an extensive biologically plausible network, have implemented on the FPGA. [24][25][26][27] NeuroFlow 28 employs a 6-FPGA system to simulate general-purpose SNNs. Network features are described by a high-level synthesis (HLS) tool and a special compiler generates register transfer level (RTL) code.…”

Section: Introductionmentioning

confidence: 99%

“…Previously, numerous FPGA‐based designs of SNN with different architectures have been investigated for various applications. Previous works, from single neurons to an extensive biologically plausible network, have implemented on the FPGA 24‐27 . NeuroFlow 28 employs a 6‐FPGA system to simulate general‐purpose SNNs.…”

Section: Introductionmentioning

confidence: 99%

A reconfigurable real‐time neuromorphic hardware for spiking winner‐take‐all network

Abdoli

Safari

2020

Circuit Theory & Apps

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The central nervous system receives a vast amount of sensory inputs, and it should be able to discriminate and recognize different kinds of multisensory information. Winner-take-all (WTA) consists of a simple recurrent neural network carrying out discrimination of input signals through competition. This paper presents a real-time scalable digital hardware implementation of the spiking WTA network. The need for concurrent computing, real-time performance, proper accuracy, and the reconfigurable device has led to the fieldprogrammable gate array (FPGA) as the target hardware platform. A set of techniques is employed to lessen memory and resource usage. The proposed architecture consists of multiprocessing elements, which share hardware resources between a specific number of neurons. We introduce a novel connectivity array for neurons (dedicated to the WTA network) to cut down memory usage. Also, a multiplier-less method in the neuron model and a novel tree adder in the synapse processing unit are designed to improve computational efficiency. The proposed network simulates 4,500 neurons in real time on a Xilinx Artix-7 FPGA, while a scalable architecture facilitates the implementation of up to 20,000 neurons on this device. The pipeline structure can guarantee real-time performance for large-scale networks. Based on simulation and physical synthesis results, the presented network mimics biological WTA dynamics and consumes efficient hardware resources.

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A Novel Nonlinear Function Evaluation Approach for Efficient FPGA Mapping of Neuron and Synaptic Plasticity Models

Cited by 18 publications

References 56 publications

Multiplierless low‐cost implementation of Hindmarsh–Rose neuron model in case of large‐scale realization

Multiplierless low‐cost implementation of Hindmarsh–Rose neuron model in case of large‐scale realization

A Raspberry Pi Based Hardware Implementations of Various Neuron Models

A reconfigurable real‐time neuromorphic hardware for spiking winner‐take‐all network

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