Essays about: "Chip Networks"
Showing result 1 - 5 of 55 essays containing the words Chip Networks.
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1. Optimizing on-chip Machine Learning for Data Prefetching
University essay from Göteborgs universitet/Institutionen för data- och informationsteknikAbstract : The idea behind data prefetching is to speed up program execution by predicting what data is needed by the processor, before it is actually needed. Data prefetching is commonly performed by prefetching the next memory address in line, but there are other, more sophisticated approaches such as machine learning. READ MORE
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2. Deep Learning Model Deployment for Spaceborne Reconfigurable Hardware : A flexible acceleration approach
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Space debris and space situational awareness (SSA) have become growing concerns for national security and the sustainability of space operations, where timely detection and tracking of space objects is critical in preventing collision events. Traditional computer-vision algorithms have been used extensively to solve detection and tracking problems in flight, but recently deep learning approaches have seen widespread adoption in non-space related applications for their high accuracy. READ MORE
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3. Performance of 2-18 GHz RF Switches Implemented in Chip & Wire Technology : Analysis of switch topologies, bias networks and an in-depth EM analysis of bondwires
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : The ability to control the path a signal takes through microwave circuitry is crucial when designing RF systems. The component that allows for the control of the signal path is called a switch, and it is the microwave component that this thesis will focus on. READ MORE
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4. Low-power Implementation of Neural Network Extension for RISC-V CPU
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Deep Learning and Neural Networks have been studied and developed for many years as of today, but there is still a great need of research on this field, because the industry needs are rapidly changing. The new challenge in this field is called edge inference and it is the deployment of Deep Learning on small, simple and cheap devices, such as low-power microcontrollers. READ MORE
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5. Low-power Acceleration of Convolutional Neural Networks using Near Memory Computing on a RISC-V SoC
University essay from Lunds universitet/Institutionen för elektro- och informationsteknikAbstract : The recent peak in interest for artificial intelligence, partly fueled by language models such as ChatGPT, is pushing the demand for machine learning and data processing in everyday applications, such as self-driving cars, where low latency is crucial and typically achieved through edge computing. The vast amount of data processing required intensifies the existing performance bottleneck of the data movement. READ MORE
