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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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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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Manjunath, Tanmay (author)
Advanced automotive vehicles are based on the real-time fusion of an increasing number of automotive sensors. For precise fusion of different sensors, measurements need to be synchronized both temporally and spatially. This thesis aims to design a hardware temporal synchronization block as part of the PRISTINE systolic array accelerator project...
master thesis 2021
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Kleijweg, Zep (author)
The recently introduced posit number system was designed as a replacement for IEEE 754 floating point, to alleviate some of its shortcomings. As the number distribution of posits is similar to the data distributions in deep neural networks (DNNs), posits offer a good alternative to fixed point numbers in DNNs: using posits can result in high...
master thesis 2021
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YANG, FANG (author)
This dissertation describes an approach to building a self-timed asynchronous pulse-mode serial link circuit. Unlike asynchronous handshake circuits or synchronous circuits, this design style does not require any feedback control blocks, which can increase latency, or any clock recovery circuits, which can increase energy consumption and...
master thesis 2021
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Preetha Vijayan, Preetha (author)
In the recent past, real-time video processing using state-of-the-art deep neural networks (DNN) has achieved human-like accuracy but at the cost of high energy consumption, making them infeasible for edge device deployment. The energy consumed by running DNNs on hardware accelerators is dominated by the number of memory read/writes and...
master 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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Li, Pai (author)
Neuromorphic electronic systems have used asynchronous logic combined with continuous-time analog circuits to emulate neurons, synapses, and learning algorithms. It is attractive because of its low power consumption and feasible implementation. Typically, the neuron firing rates are lower than the modern digital systems. Thus, the endpoints of...
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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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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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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Coenen, Joris (author)
One of the challenges of neuromorphic computing is efficiently routing spikes from neurons to their connected synapses. The aim of this thesis is to design a spike-routing architecture for flexible connections on single-chip neuromorphic systems. A model for estimating area, power consumption, memory, spike latency and link utilisation for...
master thesis 2019
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Mes, Johan (author)
The Self-Organizing Map (SOM) is an unsupervised neural networktopology that incorporates competitive learning for the classicationof data. In this thesis we investigate the design space of a system incorporating such a topology based on Spiking Neural Networks (SNNs), and apply it to classifying electrocardiogram (ECG) beats. We present novel...
master thesis 2018
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Joshi, Ayush (author)
Partial Discharges(PD) are commonly produced in defects within the insulation systems of high voltage equipment. These discharges are typically nanosecond current pulses in the amplitude range of milli-amperes. A long term exposure of the insulation system to these partial discharges accelerate the aging mechanisms that eventually lead to the...
master thesis 2017
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You, Xuefei (author)
Neuromorphic engineering, aiming at emulating neuro-biological architectures in efficient ways, has been widely studied both on com- ponent and VLSI system level. The design space of neuromorphic neuron, the basic unit to conduct signal processing and transmission in nervous system, has been widely explored while that of synapse, the specialized...
master thesis 2017
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