FPGA Based Hardware Implementation of Simple Dynamic Binary Neural Networks

2018 ◽  
pp. 647-655 ◽  
Author(s):  
Shunsuke Aoki ◽  
Seitaro Koyama ◽  
Toshimichi Saito
Keyword(s):  
Neural Networks ◽  
Hardware Implementation

scholarly journals Hardware implementation of radial-basis neural networks with Gaussian activation functions on FPGA

2021 ◽  
Author(s):  
Volodymyr Shymkovych ◽  
Sergii Telenyk ◽  
Petro Kravets
Keyword(s):  
Neural Networks ◽  
Hardware Implementation ◽  
Gaussian Function ◽  
Activation Function ◽  
Rbf Neural Networks ◽  
Activation Functions ◽  
Rbf Network ◽  
Combination Scheme ◽  
Radial Basis ◽  
Hidden Layer

AbstractThis article introduces a method for realizing the Gaussian activation function of radial-basis (RBF) neural networks with their hardware implementation on field-programmable gaits area (FPGAs). The results of modeling of the Gaussian function on FPGA chips of different families have been presented. RBF neural networks of various topologies have been synthesized and investigated. The hardware component implemented by this algorithm is an RBF neural network with four neurons of the latent layer and one neuron with a sigmoid activation function on an FPGA using 16-bit numbers with a fixed point, which took 1193 logic matrix gate (LUTs—LookUpTable). Each hidden layer neuron of the RBF network is designed on an FPGA as a separate computing unit. The speed as a total delay of the combination scheme of the block RBF network was 101.579 ns. The implementation of the Gaussian activation functions of the hidden layer of the RBF network occupies 106 LUTs, and the speed of the Gaussian activation functions is 29.33 ns. The absolute error is ± 0.005. The Spartan 3 family of chips for modeling has been used to get these results. Modeling on chips of other series has been also introduced in the article. RBF neural networks of various topologies have been synthesized and investigated. Hardware implementation of RBF neural networks with such speed allows them to be used in real-time control systems for high-speed objects.

Efficient Hardware Implementation of Cellular Neural Networks with Incremental Quantization and Early Exit

2018 ◽  
Vol 14 (4) ◽  
pp. 1-20 ◽  
Author(s):  
Xiaowei Xu ◽  
Qing Lu ◽  
Tianchen Wang ◽  
Yu Hu ◽  
Chen Zhuo ◽  
...  
Keyword(s):  
Neural Networks ◽  
Hardware Implementation ◽  
Cellular Neural Networks ◽  
Early Exit

Noise-robust hardware implementation of neural networks

2015 ◽  
Author(s):  
Vincent Canals ◽  
Miquel L. Alomar ◽  
Antoni Morro ◽  
Antoni Oliver ◽  
Josep L. Rossello
Keyword(s):  
Neural Networks ◽  
Hardware Implementation ◽  
Noise Robust

Simulation and hardware implementation of competitive learning neural networks

2008 ◽  
pp. 189-204 ◽  
Author(s):  
A. Prieto ◽  
P. Martin-Smith ◽  
J. J. Merelo ◽  
F. J. Pelayo ◽  
J. Ortega ◽  
...  
Keyword(s):  
Neural Networks ◽  
Hardware Implementation ◽  
Competitive Learning

The Faulty Behavior of Feedforward Neural Networks with Hard-Limiting Activation Function

1997 ◽  
Vol 9 (5) ◽  
pp. 1109-1126
Author(s):  
Zhiyu Tian ◽  
Ting-Ting Y. Lin ◽  
Shiyuan Yang ◽  
Shibai Tong
Keyword(s):  
Neural Networks ◽  
Monte Carlo ◽  
Artificial Neural Networks ◽  
Monte Carlo Simulations ◽  
Hardware Implementation ◽  
Fault Tolerant ◽  
Feedforward Neural Networks ◽  
Activation Function ◽  
Artificial Neural

With the progress in hardware implementation of artificial neural networks, the ability to analyze their faulty behavior has become increasingly important to their diagnosis, repair, reconfiguration, and reliable application. The behavior of feedforward neural networks with hard limiting activation function under stuck-at faults is studied in this article. It is shown that the stuck-at-M faults have a larger effect on the network's performance than the mixed stuck-at faults, which in turn have a larger effect than that of stuck-at-0 faults. Furthermore, the fault-tolerant ability of the network decreases with the increase of its size for the same percentage of faulty interconnections. The results of our analysis are validated by Monte-Carlo simulations.

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