FPGA implementation of an ANN for detection of anthelmintics resistant nematodes in sheep flocks

Rahnamaei, Ali; Pariz, Naser; Akbarimajd, Adel

doi:10.1109/iciea.2009.5138533

Cited by 9 publications

(2 citation statements)

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“…Due to their intrinsically high level of parallelism and number of connections, ANNs map efficiently also on FP-GAs [40]- [42]. In addition, ANNs are also very modular, meaning that the description of a given network, using a Hardware Description Language (HDL), becomes fairly straightforward once the neuron component has been developed.…”

Section: B Accelerating Hardware For Annsmentioning

confidence: 99%

Artificial Neural Networks for Space and Safety-Critical Applications: Reliability Issues and Potential Solutions

Rech

2024

IEEE Trans. Nucl. Sci.

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Machine Learning is among the greatest advancements in computer science and engineering and is today used to classify or detect objects, a key feature in autonomous vehicles. Since neural networks are heavily used in safety-critical applications, such as automotive and aerospace, their reliability must be paramount. However, the reliability evaluation of neural network systems is extremely challenging due to the complexity of the software, which is composed of hundreds of layers, and of the underlying hardware, typically a parallel device or an embedded accelerator.This paper reviews fundamental concepts of Artificial Intelligence, Deep Neural Network, and parallel computing device reliability. Then, the reliability studies that consider the radiation effects in the hardware, their propagation through the computing architecture, and their final impact on the software output are summarized. A detailed survey of the available strategies to measure the sensitivity of neural network frameworks and to observe fault propagation is given, together with a summary of the data obtained so far. Finally, a discussion on how to use the experimental evaluation to design effective and efficient hardening solutions for Artificial Neural Networks is provided. The available hardening solutions are critically reviewed, highlighting their benefits and drawbacks.

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Section: B Accelerating Hardware For Annsmentioning

confidence: 99%

Artificial Neural Networks for Space and Safety-Critical Applications: Reliability Issues and Potential Solutions

Rech

2024

IEEE Trans. Nucl. Sci.

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“…Several FPGA-based neural networks applied to fields of chemistry [7], [8], biology [9], [10] and medicine [11] appeared in the last couple of years and they are, generally, interested in the necessary features for developing portable applications.…”

Section: Introductionmentioning

confidence: 99%

Modeling of Pain on a FPGA-based Neural Network

Sousa¹,

Torres²

2013

Artificial Intelligence and Applications / 794: Modelling, Identification and Control / 795: Parallel and Distributed Computing

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Although unpleasant, pain is a necessary sensation. It is helpful to prevent further injuries by inducing protective reflex, withdrawal movements and to serve as an alarm of health problems to the body. Due to such importance, several studies have been made in order to understand pain mechanisms. This work presents an artificial neural network for modeling of pain implemented on a Field Programmable Gate Array (FPGA). The model is based on the Gate Control Theory of Pain and, for the purpose of allowing hardware implementation of some specific features of the model, a new method for the approximation to the activation function of the neural network was elaborated. Such proposal combines piecewise first-and second-order approximations and it is also presented in this paper.

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Artificial Neural Network Hardware Implementation: Recent Trends and Applications

Gupta

Koppad

2020

Computational Vision and Bio-Inspired Computing

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FPGA implementation of an ANN for detection of anthelmintics resistant nematodes in sheep flocks

Cited by 9 publications

References 10 publications

Artificial Neural Networks for Space and Safety-Critical Applications: Reliability Issues and Potential Solutions

Artificial Neural Networks for Space and Safety-Critical Applications: Reliability Issues and Potential Solutions

Modeling of Pain on a FPGA-based Neural Network

Artificial Neural Network Hardware Implementation: Recent Trends and Applications

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