Searched for: subject%3A%22Neuromorphic%22
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Ketelaar, Bas (author)
Artificial intelligence has a strong need for faster and more energy-efficient solutions, especially for computation performed at the sensor edge. On-chip photonic neural networks (PNNs) offer a promising solution for high speeds and energy efficiency. A less explored side of PNNs is their application to time-series data, which is often the case...
master thesis 2024
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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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Finocchio, Giovanni (author), Incorvia, Jean Anne C. (author), Friedman, Joseph S. (author), Yang, Qu (author), Giordano, Anna (author), Grollier, Julie (author), Yang, Hyunsoo (author), Cotofana, S.D. (author), Lin, Peng (author)
In the ‘Beyond Moore’s Law’ era, with increasing edge intelligence, domain-specific computing embracing unconventional approaches will become increasingly prevalent. At the same time, adopting a variety of nanotechnologies will offer benefits in energy cost, computational speed, reduced footprint, cyber resilience, and processing power. The...
review 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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Aziza, H. (author), Postel-Pellerin, J. (author), Fieback, M. (author), Hamdioui, S. (author), Xun, H. (author), Taouil, M. (author), Coulie, K. (author), Rahajandraibe, W. (author)
While Resistive RRAM (RRAM) offers attractive features for artificial neural networks (NN) such as low power operation and high-density, its conductance variation can pose significant challenges when the storage of synaptic weights is concerned. This paper reports an experimental evaluation of the conductance variations of manufactured RRAMs...
conference paper 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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Kievits, Joris (author)
The number of satellites in orbit is increasing at an accelerating pace. One major issue that currently hinders the range of satellite applications is data analysis. Most data is transmitted back to ground stations for analysis resulting in bandwidth-related issues, or the processing is performed on board, necessitating large computational power...
master thesis 2023
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Şabanoğlu, Mahir (author)
An event-based camera enables capturing a video at a high temporal resolution, high dynamical range, reduced power consumption and minimal data bandwidth while the camera has minimal physical dimensions compared to a frame-based camera with the same vision properties. The limiting factor, however, of an event-based camera is the spatial...
master thesis 2023
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Ortner, Thomas (author), Pes, Lorenzo (author), Gentinetta, Joris (author), Frenkel, C. (author), Pantazi, Angeliki (author)
Recurrent neural networks trained with the backpropagation through time (BPTT) algorithm have led to astounding successes in various temporal tasks. However, BPTT introduces severe limitations, such as the requirement to propagate information backwards through time, the weight symmetry requirement, as well as update-locking in space and time....
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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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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Ottati, F. (author), Gao, Chang (author), Chen, Qinyu (author), Brignone, Giovanni (author), Casu, Mario R. (author), Eshraghian, Jason K. (author), Lavagno, Luciano (author)
As deep learning models scale, they become increasingly competitive from domains spanning from computer vision to natural language processing; however, this happens at the expense of efficiency since they require increasingly more memory and computing power. The power efficiency of the biological brain outperforms any large-scale deep...
journal article 2023
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Kevin Shidqi, Kevin (author)
With recent breakthroughs in AI (Artificial Intelligence) technology, the impact of AI on society can be felt in various fields. The market for AI software, for example, reached a valuation of \$62 billion in 2022. A growing number of new computer architectures specialized in running these AI software were also developed. At first they were run...
master thesis 2022
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Verheyen, Jan (author)
Insects have — over millions of years of evolution — perfected many of the systems that roboticists aim to achieve; they can swiftly and robustly navigate through different environments under various conditions while at the same time being highly energy efficient. To reach this level of performance and efficiency one might want to look at and take...
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
Searched for: subject%3A%22Neuromorphic%22
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