Searched for: subject%3A%22SNN%22
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Dobriţa, Alexandra (author)
Motivated by the desire to bring intelligent processing at the Edge, enabling online learning on resource- and latency-constrained embedded devices has become increasingly appealing, as it has the potential to tackle a wide range of challenges: on the one hand, it can deal with on-the-fly adaptation to fast sensor-generated streams of data under...
master thesis 2024
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LIN, Jinhuang (author)
Recent trends in machine learning (ML) have placed a strong emphasis on power- and resource-efficient neural networks, as well as the development of neural networks on edge devices. Spiking neural net-works (SNNs), due to their event-based nature, are one of the most promising types of neural networks for low-power applications. To accelerate...
master thesis 2023
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Burgers, Tim (author)
In recent years, Artificial Neural Networks (ANN) have become a standard in robotic control. However, a significant drawback of large-scale ANNs is their increased power consumption. This becomes a critical concern when designing autonomous aerial vehicles, given the stringent constraints on power and weight. Especially in the case of blimps,...
master thesis 2023
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Dong, Yingzhou (author)
Cardiovascular diseases (CVDs) are the top cause of death worldwide, and their diagnosis can be quickly and painlessly achieved through Electrocardiogram (ECG). The diagnosis of electrocardiogram has gradually evolved from manual diagnosis by doctors to one that can be realized using Artificial Intelligence (AI). Early AI still required manual...
master thesis 2023
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Lammers, Laurens (author)
Neuromorphic sensors, like for example event cameras, detect incremental changes in the sensed quantity and communicate these via a stream of events. Desired properties of these signals such as high temporal resolution and asynchrony are not always fully exploited by algorithms that process these signals. Spiking neural networks (SNNs) have...
master thesis 2023
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Liu, Yuxiang (author)
Machine learning can be effectively applied in control loops to robustly make optimal control decisions. There is increasing interest in using spiking neural networks (SNNs) as the apparatus for machine learning in control engineering, because SNNs can potentially offer high energy efficiency and new SNN-enabling neuromorphic hardwares are being...
master thesis 2023
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Nembhani, Prithvish Vijaykumar (author)
Artificial intelligence, machine learning, and deep learning have been the buzzwords in almost every industry (medical, automotive, defense, security, finance, etc.) for the last decade. As the market moves towards AI-based solutions, so does the computation need for these solutions increase and change with time. With the rise of smart cities...
master thesis 2023
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Du, Tianyu (author)
Spiking Neural Networks(SNN) have been widely leveraged by neuromorphic systems due to their ability to closely mimic biological neural behavior, where information is exchanged and received between neurons in the form of sparse events(spikes). Such neuromorphic systems are highly energy-efficient because the use of a global clock can be avoided...
master thesis 2023
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Huang, Jiongyu (author)
A Spiking neural network (SNN) is a type of artificial neural network which encodes information using spike timing, network structure, and synaptic weights to emulate the information processing function of the human brain. Within an SNN, it is always required to support the spike transmission that travels between neurons(array). This thesis aims...
master thesis 2023
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Stroobants, S. (author), de Wagter, C. (author), de Croon, G.C.H.E. (author)
Neuromorphic processing promises high energy efficiency and rapid response rates, making it an ideal candidate for achieving autonomous flight of resource-constrained robots. It can be especially beneficial for complex neural networks as are used for high-level visual perception. However, fully neuromorphic solutions also need to tackle low...
conference paper 2023
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Narayanan, Shyam (author), Cartiglia, Matteo (author), Rubino, Arianna (author), Lego, Charles (author), Frenkel, C. (author), Indiveri, Giacomo (author)
Low-power event-based analog front-ends (AFE) are a crucial component required to build efficient end-to-end neuromorphic processing systems for edge computing. Although several neuromorphic chips have been developed for implementing spiking neural networks (SNNs) and solving a wide range of sensory processing tasks, there are only a few general...
conference paper 2023
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Zhang, Jinyao (author)
Spiking Neural Networks use Address Event Representation to communicate among different Neuron Arrays. To mimic the behavior of the human neural system and meets the requirement for large Neuron Array communication, the AER interconnect should be area-saving, have low power, and operates at high speed.<br/>This thesis aims to build self-timed...
master thesis 2022
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Long, Jinyun (author)
As the new generation of neural networks, Spiking Neural Network architectures<br/>executes on specialized Neuromorphic devices. The mapping of Spiking Neural Network architectures affects the power consumption and performance of the system. The target platform of the thesis is a hardware platform with Neuromorphic Arrays with columns for neural...
master thesis 2022
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Yang, Yichen (author)
To support the spike propagates between neurons, neuromorphic computing systems always require a high-speed communication link. <br/>Meanwhile, spiking neural networks are event-driven so that the communication links normally exclude the clock signal and related blocks. This thesis aims to develop a self-timed off-chip interconnect system with...
master thesis 2022
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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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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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Chen, Qinyu (author), Gao, C. (author), Fu, Yuxiang (author)
Spiking neural networks (SNNs) are promising alternatives to artificial neural networks (ANNs) since they are more realistic brain-inspired computing models. SNNs have sparse neuron firing over time, i.e., spatiotemporal sparsity; thus, they are helpful in enabling energy-efficient hardware inference. However, exploiting the spatiotemporal...
journal article 2022
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El Arrassi, A.E. (author), Gebregiorgis, A.B. (author), Haddadi, Anass El (author), Hamdioui, S. (author)
Spiking Neural Networks (SNNs) can drastically improve the energy efficiency of neuromorphic computing through network sparsity and event-driven execution. Thus, SNNs have the potential to support practical cognitive tasks on resource constrained platforms, such as edge devices. To realize this, SNN requires energy-efficient hardware which can...
conference paper 2022
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Stroobants, S. (author), Dupeyroux, J.J.G. (author), de Croon, G.C.H.E. (author)
The great promises of neuromorphic sensing and processing for robotics have led researchers and engineers to investigate novel models for robust and reliable control of autonomous robots (navigation, obstacle detection and avoidance, etc.), especially for quadrotors in challenging contexts such as drone racing and aggressive maneuvers. Using...
conference paper 2022
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Stroobants, S. (author), Dupeyroux, J.J.G. (author), de Croon, G.C.H.E. (author)
Compelling evidence has been given for the high energy efficiency and update rates of neuromorphic processors, with performance beyond what standard Von Neumann architectures can achieve. Such promising features could be advantageous in critical embedded systems, especially in robotics. To date, the constraints inherent in robots (e.g., size...
journal article 2022
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