FPGA-based implementation of deep neural network using stochastic computing
M Nobari, H Jahanirad - Applied Soft Computing, 2023 - Elsevier
A serious challenge in artificial real-time applications is the hardware implementation of
deep neural networks (DNN). Among various methods, stochastic computing (SC)-based …
deep neural networks (DNN). Among various methods, stochastic computing (SC)-based …
Artificial neural networks model design of Lorenz chaotic system for EEG pattern recognition and prediction
L Zhang - 2017 IEEE Life Sciences Conference (LSC), 2017 - ieeexplore.ieee.org
This paper presents the preliminary work of a multidisciplinary brain research program. The
goal of this research program is to generate accurate and effective signals for non-invasive …
goal of this research program is to generate accurate and effective signals for non-invasive …
Implementation framework for artificial neural networks on fpga
In an Artificial Neural Network (ANN) a large number of highly interconnected simple
nonlinear processing units work in parallel to solve a specific problem. Parallelism …
nonlinear processing units work in parallel to solve a specific problem. Parallelism …
Hardware implementation of evolvable block-based neural networks utilizing a cost efficient sigmoid-like activation function
This paper presents the hardware implementation of an evolvable block-based neural
network that utilizes a novel and cost efficient sigmoid-like activation function. Evolvable …
network that utilizes a novel and cost efficient sigmoid-like activation function. Evolvable …
A high performance full pipelined arquitecture of MLP neural networks in FPGA
APA Ferreira, ENS Barros - 2010 17th IEEE International …, 2010 - ieeexplore.ieee.org
This paper presents an architecture for a FPGA based implementation of a Multilayer
Perceptron Artificial Neural Network (ANN). The proposed architecture aims to support the …
Perceptron Artificial Neural Network (ANN). The proposed architecture aims to support the …
Optimized FPGA Implementation of an Artificial Neural Network Using a Single Neuron
Y Gorbounov, H Chen - … Conference on Computer Science and Education …, 2023 - Springer
Since its emergence in the early 1940s as a connectionist approximation of the functioning
of neurons in the brain, artificial neural networks have undergone significant development …
of neurons in the brain, artificial neural networks have undergone significant development …
Evolvable Block‐Based Neural Network Design for Applications in Dynamic Environments
SG Merchant, GD Peterson - VLSI Design, 2010 - Wiley Online Library
Dedicated hardware implementations of artificial neural networks promise to provide faster,
lower‐power operation when compared to software implementations executing on …
lower‐power operation when compared to software implementations executing on …
A “learning by design” application for modeling, implementing, and evaluating hardware architectures for artificial neural networks at undergraduate level
E Guzmán‐Ramírez, I Garcia… - Computer Applications …, 2019 - Wiley Online Library
Artificial intelligence is currently playing an important role in engineering education. Artificial
neural networks (ANNs), for example, are one of the most important topics in computer …
neural networks (ANNs), for example, are one of the most important topics in computer …
[PDF][PDF] 基于FPGA 的人工神经网络实现方法的研究
杨银涛， 汪海波， 张志， 周建华 - 2009 - core.ac.uk
基于FPGA 的神经网络实现方法已成为实际实时应用神经网络的一种途径. 本文就十多年来基于
FPGA 的ANN 实现作一个系统的总结, 例举关键的技术问题, 给出详细的数据分析 …
FPGA 的ANN 实现作一个系统的总结, 例举关键的技术问题, 给出详细的数据分析 …
Neural networks for real-time adaptive beamforming in simultaneous transmit and receive digital phased arrays: Student submission
Fully digital phased arrays have become more common as technology advancements have
driven down their cost. However, adaptive beamforming techniques can lead to extremely …
driven down their cost. However, adaptive beamforming techniques can lead to extremely …