Proceedings ArticleDOI
FPGA implementation of a neural network for a real-time hand tracking system
M. Krips,T. Lammert,Anton Kummert +2 more
- pp 313-317
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TLDR
A real-time localization and tracking algorithm has been developed for detecting human hands in video images using a single-pixel-based classification, so that a continuous data stream can be processed.Abstract:
The advantage of parallel computing of artificial neural networks can be combined with the potentials of VLSI circuits in order to design a real time detection and tracking system applied to video images. Based on these facts, a real-time localization and tracking algorithm has been developed for detecting human hands in video images. Due to the real time aspect, a single-pixel-based classification is aspired, so that a continuous data stream can be processed. Consequently, no storage of full images or parts of them is necessary. The classification, whether a pixel belongs to a hand or to the background, is done by analyzing the RGB-values of a single pixel by means of an artificial neural network. To obtain the full processing speed of this neural network a hardware solution is realized in a Field Programmable Gate Array (FPGA).read more
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Journal ArticleDOI
Artificial neural networks in hardware: A survey of two decades of progress
Janardan Misra,Indranil Saha +1 more
TL;DR: This article presents a comprehensive overview of the hardware realizations of artificial neural network models, known as hardware neural networks (HNN), appearing in academic studies as prototypes as well as in commercial use.
Posted Content
A Survey of Neuromorphic Computing and Neural Networks in Hardware.
Catherine D. Schuman,Thomas E. Potok,Robert M. Patton,J. Douglas Birdwell,Mark Edward Dean,Garrett S. Rose,James S. Plank +6 more
TL;DR: An exhaustive review of the research conducted in neuromorphic computing since the inception of the term is provided to motivate further work by illuminating gaps in the field where new research is needed.
Journal ArticleDOI
Hardware Implementation of a Real-Time Neural Network Controller With a DSP and an FPGA for Nonlinear Systems
Seul Jung,Sung-Su Kim +1 more
TL;DR: The designed intelligent control hardware can perform real-time control of the backpropagation learning algorithm of a neural network and becomes cost effective by using a high capacity of an FPGA chip.
Journal ArticleDOI
Efficient digital implementation of the sigmoid function for reprogrammable logic
TL;DR: Four previously published piecewise linear and one piecewise second-order approximation of the sigmoid function are compared with SIG-sigmoid, a purely combinational approximation and it is concluded that the best performance is achieved by SIG-Sigmoid.
Proceedings ArticleDOI
Efficient hardware implementation of the hyperbolic tangent sigmoid function
TL;DR: This paper presents a simple and efficient architecture for digital hardware implementation of the hyperbolic tangent sigmoid function, which proves to be more efficient considering area × delay as a performance metric when compared to similar proposals.
References
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Book
Neural Networks: A Comprehensive Foundation
TL;DR: Thorough, well-organized, and completely up to date, this book examines all the important aspects of this emerging technology, including the learning process, back-propagation learning, radial-basis function networks, self-organizing systems, modular networks, temporal processing and neurodynamics, and VLSI implementation of neural networks.
MonographDOI
Parallel Distributed Processing: Explorations in the Microstructure of Cognition: Foundations
Journal ArticleDOI
The self-organizing map
TL;DR: The self-organizing map, an architecture suggested for artificial neural networks, is explained by presenting simulation experiments and practical applications, and an algorithm which order responses spatially is reviewed, focusing on best matching cell selection and adaptation of the weight vectors.
Journal ArticleDOI
Training feedforward networks with the Marquardt algorithm
TL;DR: The Marquardt algorithm for nonlinear least squares is presented and is incorporated into the backpropagation algorithm for training feedforward neural networks and is found to be much more efficient than either of the other techniques when the network contains no more than a few hundred weights.
Book
Numerical Methods for Unconstrained Optimization and Nonlinear Equations (Classics in Applied Mathematics, 16)
TL;DR: In this paper, Schnabel proposed a modular system of algorithms for unconstrained minimization and nonlinear equations, based on Newton's method for solving one equation in one unknown convergence of sequences of real numbers.
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