Sökning: "Deep Learning Accelerators"

Visar resultat 1 - 5 av 6 uppsatser innehållade orden Deep Learning Accelerators.

1. 1. Deep Learning Model Deployment for Spaceborne Reconfigurable Hardware : A flexible acceleration approach

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Javier Ferre Martin; [2023]
   Nyckelord :Space Situational Awareness; Deep Learning; Convolutional Neural Networks; FieldProgrammable Gate Arrays; System-On-Chip; Computer Vision; Dynamic Partial Reconfiguration; High-Level Synthesis; Rymdsituationstänksamhet; Djupinlärning; Konvolutionsnätverk; Omkonfigurerbara Field-Programmable Gate Arrays FPGAs ; System-On-Chip SoC ; Datorseende; Dynamisk partiell omkonfigurering; Högnivåsyntes.;

   Sammanfattning : 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. LÄS MER

3. 2. Low-power Implementation of Neural Network Extension for RISC-V CPU

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Dario Lo Presti Costantino; [2023]
   Nyckelord :Artificial intelligence; Deep learning; Neural networks; Edge computing; Convolutional neural networks; Low-power electronics; RISC-V; AI accelerators; Parallel processing; Artificiell intelligens; Deep learning; Neurala nätverk; Edge computing; konvolutionella neurala nätverk; Lågeffektelektronik; RISC-V; AI-acceleratorer; Parallell bearbetning;

   Sammanfattning : 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. LÄS MER

4. 3. Low Power Hardware Accelerator For Gated Recurrent Unit

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Malavika Balakumar; [2022]
   Nyckelord :;

   Sammanfattning : Neural Networks are a subset of Machine Learning which are designed to recognize patterns. Recurrent Neural Networks are an important part of AI (Artificial Intelligence) which allows for short term as well as long term dependencies to be captured. LÄS MER

5. 4. Implementation of a Deep Learning Inference Accelerator on the FPGA.

   Master-uppsats, Lunds universitet/Institutionen för elektro- och informationsteknik

   Författare :Shenbagaraman Ramakrishnan; [2020]
   Nyckelord :Artificial Intelligence; Machine Learning; Deep Learning; Neural Networks; Deep Learning Accelerators; NVDLA; FPGA; Technology and Engineering;

   Sammanfattning : Today, Artificial Intelligence is one of the most important technologies, ubiquitous in our daily lives. Deep Neural Networks (DNN's) have come up as state of art for various machine intelligence applications such as object detection, image classification, face recognition and performs myriad of activities with exceptional prediction accuracy. LÄS MER

7. 5. Accelerating CNN on FPGA : An Implementation of MobileNet on FPGA

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Yulan Shen; [2019]
   Nyckelord :CNN; FPGA acceleration; Deep Learning; MobileNet; Image classification; Computer vision;

   Sammanfattning : Convolutional Neural Network is a deep learning algorithm that brings revolutionary impact on computer vision area. One of its applications is image classification. However, problem exists in this algorithm that it involves huge number of operations and parameters, which limits its possibility in time and resource restricted embedded applications. LÄS MER