Searched for: subject%3A%22Spiking%255C%2BNeural%255C%2BNetworks%22
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Zhou, Yongkang (author)
Spiking neural networks (SNN), as the third-generation artificial neural network, has a similar potential pulse triggering mechanism to the biological neuron. This mechanism enables the spiking neural network to increase computing power compared to the traditional artificial neural network to process complex information. However, a large number...
master thesis 2022
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Buis, Jan Maarten (author)
Renewed interest in memory technologies such as memristors and ferroelectric devices can provide opportunities for traditional and non-traditional computing systems alike. To make versatile, reprogrammable AI hardware possible, neuromorphic systems are in need of a low-power, non-volatile and analog memory solution to store the weights of the...
master thesis 2022
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LU, Jingyi (author)
Inspired by the natural nervous system, synaptic plasticity rules are applied to train spiking neural networks. Different from learning algorithms such as propagation and evolution that are widely used to train spiking neural networks, synaptic plasticity rules learn the parameters with local information, making them suitable for online learning...
master thesis 2022
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Rudge, Zacharia (author)
With recent breakthroughs in AI and deep learning, applying these techniques to on-board computers for space applications has grown in interest to engineers on space applications. The space field brings its own challenges, such as reliability and power restrictions. The proposed solution in this work concerns a neuromorphic accelerator for a...
master thesis 2021
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van Otterloo, Bas (author)
Radar systems have been used for decades to detect targets on the ground and in the air. The radar signal is transformed into a range-doppler image that distinguishes each detected object by range and velocity for further processing. A target detection algorithm is used to filter noise and clutter. Each target can be in a region with a different...
master thesis 2021
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Ma, Hanyu (author)
Hardware cryptographic algorithm implementation is easy to attack by side-channel attacks. The power-based side-channel attacks are powerful among several side-channel attacks. This attack methods use the relationship between the leakage model and power traces to reveal the secret key. Some existing countermeasures like mask and hide can protect...
master thesis 2021
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Gonzalez Alvarez, Marina (author)
Micro robotic airships offer significant advantages in terms of safety, mobility, and extended flight times. However, their highly restrictive weight constraints pose a major challenge regarding the available computational power to perform the required control tasks. Thus, spiking neural networks (SNNs) are a promising research direction. By...
master thesis 2021
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Wang, H. (author)
The human brain is a natural high-performance computing systemwith outstanding properties, e.g., ultra-low energy consumption, highly parallel information processing, suitability for solving complex tasks, and robustness. As such, numerous attempts have been made to devise neuromorphic systems able to achieve brain-akin computation abilities,...
doctoral thesis 2021
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Hijlkema, Sybold (author)
Mobile devices are getting increasingly powerful, becoming compatible<br/>for an ever increasing set of functionality. Applications based around<br/>neural networks however still have to offload parts of their computations<br/>to the cloud since current Artificial Neural Networks (ANNs) are<br/>still too computationally expensive for any...
master thesis 2021
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Arriëns, Roy (author)
A big catalyst of the AI revolution has been Artificial Neural Networks (ANN), abstract computation models based on the biological neural networks in the brain. However, they require an immense amount of computational resources and power to configure and when deployed often are dependent on cloud resources to function. This makes ANNs less...
master thesis 2021
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Kshirasagar, Shreya Sanjeev (author)
As we move towards edge computing, not only low power but concurrently, critical timing is demanded from the underlying hardware platform. Spiking neural networks ensure high performance and low power when run on specialized architectures like neuromorphic hardware. However, the techniques in use to configure these neural networks on massively...
master thesis 2021
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Prozée, Randy (author)
The development of the Spiking Neural Network (SNN) offers great potential in combination with new types of event-based sensors, by exploiting the embedded temporal information. When combined with dedicated neuromorphic hardware it enables ultra-low power solutions and local on-chip learning. This work implements and presents a viable...
master thesis 2021
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de Gelder, Luuk (author)
Conversion from digital information to spike trains is needed for Spiking Neural Networks. Moreover, it is one of the most important steps for Spiking Neural Networks. This conversion could lead to much information loss depending on which encoding algorithm is used. Another major problem that can occur in a specific use-case is the limited...
master thesis 2021
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Wang, H. (author), Cucu Laurenciu, N. (author), Cotofana, S.D. (author)
In the paper we propose a reconfigurable graphene-based Spiking Neural Network (SNN) architecture and a training methodology for initial synaptic weight values determination. The proposed graphene-based platform is flexible, comprising a programmable synaptic array which can be configured for different initial synaptic weights and plasticity...
journal article 2021
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van Wezel, Martijn (author)
Neurons in Spiking Neural Networks (SNNs) communicate through spikes, similarly that neurons in the brain communicate, thus mimicking the brain. The working of SNNs is temporally based, as the spikes are time-dependent. SNNs have the benefit to perform continual classification, and are inherently more low-power than other Artificial Neural...
master thesis 2020
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Hagenaars, J.J. (author), Paredes-Vallés, Federico (author), Bohté, Sander M. (author), de Croon, G.C.H.E. (author)
Flying insects are capable of vision-based navigation in cluttered environments, reliably avoiding obstacles through fast and agile maneuvers, while being very efficient in the processing of visual stimuli. Meanwhile, autonomous micro air vehicles still lag far behind their biological counterparts, displaying inferior performance at a much...
journal article 2020
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Büller, Bas (author)
Spiking neural networks are notoriously hard to train because of their complex dynamics and sparse spiking signals. However, in part due to these properties, spiking neurons possess high computa- tional power and high theoretical energy efficiency. This thesis introduces an online, supervised, and gradient-based learning algorithm for spiking...
master thesis 2020
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Hagenaars, Jesse (author)
Flying insects are capable of autonomous vision-based navigation in cluttered environments, reliably avoiding objects through fast and agile manoeuvres. Meanwhile, insect-scale micro air vehicles still lag far behind their biological counterparts, displaying inferior performance at a fraction of the energy efficiency. In light of this, it is in...
master thesis 2020
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Wang, He (author), Cucu Laurenciu, N. (author), Jiang, Y. (author), Cotofana, S.D. (author)
To fully unleash the potential of graphene-based devices for neuromorphic computing, we propose a graphene synapse and a graphene neuron that form together a basic Spiking Neural Network (SNN) unit, which can potentially be utilized to implement complex SNNs. Specifically, the proposed synapse enables two fundamental synaptic functionalities,...
journal article 2020
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Spessot, Davide (author)
Recent trends in platforms for the consumer market increased the need for low-power and reliable classification engines. Spiking Neural Network (SNN) is a new technology that promises to deliver 4 orders of magnitude more performance per watt than competing solutions. Moreover, the adoption of RADAR for gesture detection provides higher...
master thesis 2019
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