Sökning: "Spikande neurala nätverk"
Visar resultat 1 - 5 av 11 uppsatser innehållade orden Spikande neurala nätverk.
1. Adversarial robustness of STDP-trained spiking neural networks
Kandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Adversarial attacks on machine learning models are designed to elicit the wrong behavior from the model. One such attack on image classifiers are maliciously crafted inputs that, to the human eye, look untampered with but have been carefully altered to cause misclassification. LÄS MER
2. Comparing energy efficiency of Leaky integrate-and-fire and Spike response neuron models in Spiking Neural Networks
Kandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Spiking Neural Networks (SNNs) are a type of neural network that is designed to mimic the way neurons function in our brains. While there have been notable advancements in developing SNNs, energy consumption hasn't been studied to the same extent. This gets especially relevant with steadily increasing network sizes. LÄS MER
3. Introducing GA-SSNN: A Method for Optimizing Stochastic Spiking Neural Networks : Scaling the Edge User Allocation Constraint Satisfaction Problem with Enhanced Energy and Time Efficiency
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : As progress within Von Neumann-based computer architecture is being limited by the physical limits of transistor size, neuromorphic comuting has emerged as a promising area of research. Neuromorphic hardware tends to be substantially more power efficient by imitating the aspects of computations in networks of neurons in the brain. LÄS MER
4. Neuromorphic Medical Image Analysis at the Edge : On-Edge training with the Akida Brainchip
Kandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Computed Tomography (CT) scans play a crucial role in medical imaging, allowing neuroscientists to identify intracranial pathologies such as haemorrhages and malignant tumours in the brain. This thesis explores the potential of deep learning models as an aid in intracranial pathology detection through medical imaging. LÄS MER
5. Exploring Column Update Elimination Optimization for Spike-Timing-Dependent Plasticity Learning Rule
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Hebbian learning based neural network learning rules when implemented on hardware, store their synaptic weights in the form of a two-dimensional matrix. The storage of synaptic weights demands large memory bandwidth and storage. LÄS MER
