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Usa, Lyana (author)Olfactory learning in <i>Drosophila </i>larvae exemplifies efficient neural processing in a small-scale network with minimal power consumption. This system enables larvae to anticipate important outcomes based on new and familiar odor stimuli, a process crucial for survival and adaptation. Central to this learning mechanism is the olfactory...master thesis 2024
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Hettema, Bart (author)Neuromorphic computing can be used to efficiently implement spiking neural networks.<br/>Such spiking neural networks can be used in edge AI applications, where low power consumption is paramount.<br/>The use of analog components allows for extremely low power implementations.<br/>This thesis contributes the designs of an analog spike generator,...master thesis 2024
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Hueber, Paul (author)Designing processors for implantable closed-loop neuromodulation systems presents a formidable challenge owing to the constrained operational environment, which requires low latency and high energy efficacy. Previous benchmarks have provided limited insights into power consumption and latency. However, this study introduces algorithmic metrics...master thesis 2024
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Marangalu, Milad Ghavipanjeh (author), Kurdkandi, Naser Vosoughi (author), Monfared, Kourosh Khalaj (author), Talebian, Iman (author), Neyshabouri, Yousef (author), Vahedi, H (author)Switched capacitor multilevel inverter topologies are attractive among industrial power electronics researchers due to their applicability in sustainable energy systems such as renewable energy source (RES) applications. In this paper, a new switched capacitor (SC)-based grid-tied seven-level inverter is proposed for renewable energy sources ...journal article 2024
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Xu, Yingfu (author), Shidqi, Kevin (author), van Schaik, Gert-Jan (author), Bilgic, Refik (author), Dobrita, Alexandra (author), Wang, Shenqi (author), Gebregiorgis, A.B. (author), Hamdioui, S. (author), Yousefzadeh, Amirreza (author)Neuromorphic processors promise low-latency and energy-efficient processing by adopting novel brain-inspired design methodologies. Yet, current neuromorphic solutions still struggle to rival conventional deep learning accelerators' performance and area efficiency in practical applications. Event-driven data-flow processing and near/in-memory...journal article 2024
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Hueber, Paul (author), Tang, Guangzhi (author), Sifalakis, Manolis (author), Liaw, Hua Peng (author), Micheli, A. (author), Tömen, N. (author), Liu, Y. (author)Designing processors for implantable closed-loop neuromodulation systems presents a formidable challenge owing to the constrained operational environment, which requires low latency and high energy efficacy. Previous benchmarks have provided limited insights into power consumption and latency. However, this study introduces algorithmic...journal article 2024
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Shokri, M. (author), Gogliettino, Alex R. (author), Hottowy, Paweł (author), Sher, Alexander (author), Litke, Alan M. (author), Chichilnisky, E. J. (author), Pequito, Sérgio (author), Muratore, D.G. (author)Objective. Bi-directional electronic neural interfaces, capable of both electrical recording and stimulation, communicate with the nervous system to permit precise calibration of electrical inputs by capturing the evoked neural responses. However, one significant challenge is that stimulation artifacts often mask the actual neural signals. To...journal article 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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Paredes-Vallés, Federico (author)In the ever-evolving landscape of robotics, the quest for advanced synthetic machines that seamlessly integrate with human lives and society becomes increasingly paramount. At the heart of this pursuit lies the intrinsic need for these machines to perceive, understand, and navigate their surroundings autonomously. Among the senses, vision...doctoral thesis 2023
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Huijbregts, Lucas (author)Ultra-low power Edge AI hardware is in increasing demand due to the battery-limited energy budget of typical Edge devices such as smartphones, wearables, and IoT sensor systems. For this purpose, this Thesis introduces an ultra-low power event-driven SRAM-based Compute In-Memory (CIM) accelerator optimized for inference of Binary Spiking Neural...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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Landbrug, Yawende (author)Abstract—This paper investigates the performance of commonly used spike detection algorithms (Absolute Amplitude Thresholding and Non-linear Energy Operator) on compressed neural signals using a novel wired-OR lossy compression algorithm. Performing compression with the wired-OR architecture mainly removes the noisy baseline and preserves spikes...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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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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- Abunahla, H.N. (author) contribution to periodical 2023
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Bierkens, G.N.J.C. (author), Grazzi, S. (author), van der Meulen, F.H. (author), Schauer, M.R. (author)We construct a new class of efficient Monte Carlo methods based on continuous-time piecewise deterministic Markov processes (PDMPs) suitable for inference in high dimensional sparse models, i.e. models for which there is prior knowledge that many coordinates are likely to be exactly 0. This is achieved with the fairly simple idea of endowing...journal article 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