[HTML][HTML] Recent advances in physical reservoir computing: A review
Reservoir computing is a computational framework suited for temporal/sequential data
processing. It is derived from several recurrent neural network models, including echo state …
processing. It is derived from several recurrent neural network models, including echo state …
A survey of neuromorphic computing and neural networks in hardware
Neuromorphic computing has come to refer to a variety of brain-inspired computers, devices,
and models that contrast the pervasive von Neumann computer architecture. This …
and models that contrast the pervasive von Neumann computer architecture. This …
The promise and challenge of stochastic computing
Stochastic computing (SC) is an unconventional method of computation that treats data as
probabilities. Typically, each bit of an N-bit stochastic number (SN) Xis randomly chosen to …
probabilities. Typically, each bit of an N-bit stochastic number (SN) Xis randomly chosen to …
Artificial neural networks in hardware: A survey of two decades of progress
This article presents a comprehensive overview of the hardware realizations of artificial
neural network (ANN) models, known as hardware neural networks (HNN), appearing in …
neural network (ANN) models, known as hardware neural networks (HNN), appearing in …
A phylogenetic, ontogenetic, and epigenetic view of bio-inspired hardware systems
If one considers life on Earth since its very beginning, three levels of organization can be
distinguished: the phylogenetic level concerns the temporal evolution of the genetic …
distinguished: the phylogenetic level concerns the temporal evolution of the genetic …
A new stochastic computing methodology for efficient neural network implementation
This paper presents a new methodology for the hardware implementation of neural networks
(NNs) based on probabilistic laws. The proposed encoding scheme circumvents the …
(NNs) based on probabilistic laws. The proposed encoding scheme circumvents the …
Development and implementation of parameterized FPGA-based general purpose neural networks for online applications
A Gomperts, A Ukil, F Zurfluh - IEEE Transactions on Industrial …, 2010 - ieeexplore.ieee.org
This paper presents the development and implementation of a generalized backpropagation
multilayer perceptron (MLP) architecture described in VLSI hardware description language …
multilayer perceptron (MLP) architecture described in VLSI hardware description language …
A stochastic-based FPGA controller for an induction motor drive with integrated neural network algorithms
This paper applies stochastic theory to the design and implementation of field-oriented
control of an induction motor drive using a single field-programmable gate array (FPGA) …
control of an induction motor drive using a single field-programmable gate array (FPGA) …
[PDF][PDF] Artificial neural network implementation on a single FPGA of a pipelined on-line backpropagation
R Gadea, J Cerdá, F Ballester… - Proceedings of the 13th …, 2000 - websrv.cecs.uci.edu
The paper describes the implementation of a systolic array for a multilayer perceptron on a
Virtex XCV400 FPGA with a hardware-friendly learning algorithm. A pipelined adaptation of …
Virtex XCV400 FPGA with a hardware-friendly learning algorithm. A pipelined adaptation of …
[图书][B] Turing's connectionism: an investigation of neural network architectures
C Teuscher - 2002 - books.google.com
Alan Mathison Turing (1912-1954) was the first to carry out substantial re search in the field
now known as Artificial Intelligence (AI). He was thinking about machine intelligence at least …
now known as Artificial Intelligence (AI). He was thinking about machine intelligence at least …