On training efficiency and computational costs of a feed forward neural network: A review
A comprehensive review on the problem of choosing a suitable activation function for the
hidden layer of a feed forward neural network has been widely investigated. Since the …
hidden layer of a feed forward neural network has been widely investigated. Since the …
Leveraging neuro-inspired AI accelerator for high-speed computing in 6G networks
The field of wireless communication is currently being pushed to new boundaries with the
emergence of 6G technology. This advanced technology requires substantially increased …
emergence of 6G technology. This advanced technology requires substantially increased …
A novel approximation methodology and its efficient vlsi implementation for the sigmoid function
In this brief, a novel approximation method and its optimized hardware implementation are
proposed for the sigmoid function used in Deep Neural Networks (DNNs). Based on …
proposed for the sigmoid function used in Deep Neural Networks (DNNs). Based on …
Efficient hardware implementation of DNN-based speech enhancement algorithm with precise sigmoid activation function
This brief presents the hardware implementation of deep neural network-based speech
enhancement algorithm (DNN-SEA) with a precise sigmoid activation function. Further, an …
enhancement algorithm (DNN-SEA) with a precise sigmoid activation function. Further, an …
A cost-efficient digital esn architecture on fpga for ofdm symbol detection
The echo state network (ESN) is a recently developed machine-learning paradigm whose
processing capabilities rely on the dynamical behavior of recurrent neural networks. Its …
processing capabilities rely on the dynamical behavior of recurrent neural networks. Its …
Accelerating next-g wireless communications with fpga-based ai accelerators
5G and beyond 5G wireless communication has revolutionized our daily lives. However, the
increased bandwidth and data rate transfer in 5G present challenges, particularly in the …
increased bandwidth and data rate transfer in 5G present challenges, particularly in the …
Low-error digital hardware implementation of artificial neuron activation functions and their derivative
In this paper we propose a low-error approximation of the sigmoid function and hyperbolic
tangent, which are mainly used to activate the artificial neuron, based on the piecewise …
tangent, which are mainly used to activate the artificial neuron, based on the piecewise …
[PDF][PDF] Hardware implementation of hyperbolic tangent and sigmoid activation functions
Z Hajduk - Bulletin of the Polish Academy of Sciences. Technical …, 2018 - bibliotekanauki.pl
This paper presents the high accuracy hardware implementation of the hyperbolic tangent
and sigmoid activation functions for artificial neural networks. A kind of a direct …
and sigmoid activation functions for artificial neural networks. A kind of a direct …
Accuracy controlled iterative method for efficient sigmoid function approximation
SR Chiluveru, M Tripathy, B Mohapatra - Electronics Letters, 2020 - Wiley Online Library
The sigmoid activation function plays an essential role in implementing neural networks in
hardware. However, due to a high degree of non‐linearity, the hardware implementation of …
hardware. However, due to a high degree of non‐linearity, the hardware implementation of …
Reconfigurable field‐programmable gate array‐based on‐chip learning neuromorphic digital implementation for nonlinear function approximation
M Gholami, E Zaman Farsa… - International Journal of …, 2021 - Wiley Online Library
Hardware implementations of spiking neural networks, which are known as neuromorphic
architectures, provide an explicit understanding of brain performance. As a result, biological …
architectures, provide an explicit understanding of brain performance. As a result, biological …