Searched for: subject%3A%22spiking%255C%2Bneural%255C%2Bnetworks%22
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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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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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Liu, Y. (author), Pan, W. (author)
Machine learning can be effectively applied in control loops to make optimal control decisions robustly. 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 hardware is being...
journal article 2023
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Eggers, Yvonne (author)
Event cameras and spiking neural networks (SNNs) allow for a highly bio-inspired, low-latency and power efficient implementation of optic flow estimation. Just recently, a hierarchical SNN was proposed in which motion selectivity is learned from raw event data in an unsupervised manner using spike-timing-dependent plasticity (STDP). However,...
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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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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Stroobants, S. (author), Dupeyroux, J.J.G. (author), de Croon, G.C.H.E. (author)
ompelling 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 and...
journal article 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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Phusakulkajorn, W. (author), Hendriks, J.M. (author), Moraal, J. (author), Dollevoet, R.P.B.J. (author), Li, Z. (author), Nunez, Alfredo (author)
In this paper, a fuzzy interval-based method is proposed for solving the problem of rail defect detection relying on an on-board measurement system and a multiple spiking neural network architecture. Instead of outputting binary values (defect or not defect), all data will belong to both classes with different spreads that are given by two fuzzy...
conference paper 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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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
Searched for: subject%3A%22spiking%255C%2Bneural%255C%2Bnetworks%22
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