Sökning: "Network processor"

Visar resultat 1 - 5 av 111 uppsatser innehållade orden Network processor.

1. 1. Trainable Region of Interest Prediction: Hard Attention Framework for Hardware-Efficient Event-Based Computer Vision Neural Networks on Neuromorphic Processors

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

   Författare :Cina Arjmand; [2023]
   Nyckelord :Artifical Intelligence; Machine Learning; Neuromorphic Engineering; Computer Vision; Technology and Engineering;

   Sammanfattning : Neuromorphic processors are a promising new type of hardware for optimizing neural network computation using biologically-inspired principles. They can effectively leverage information sparsity such as in images from event-based cameras, and are well-adapted to processing event-based data in an energy-efficient fashion. LÄS MER

3. 2. Low-power Acceleration of Convolutional Neural Networks using Near Memory Computing on a RISC-V SoC

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

   Författare :Kristoffer Westring; Linus Svensson; [2023]
   Nyckelord :FPGA; ASIC; Near Memory Computing; RISC-V; Convolutional Neural Network; Technology and Engineering;

   Sammanfattning : 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. LÄS MER

4. 3. Neuromorphic Medical Image Analysis at the Edge : On-Edge training with the Akida Brainchip

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

   Författare :Ebba Bråtman; Lucas Dow; [2023]
   Nyckelord :;

   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. 4. Introducing Machine Learning in a Vectorized Digital Signal Processor

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

   Författare :Linnéa Ridderström; [2023]
   Nyckelord :Digital Signal Processor DSP ; Application-Specific Integrated Circuit ASIC ; Machine Learning; Deep Learning; Convolutional Neural Network CNN ; Very Long Instruction Word VLIM ; Single Instruction Multiple Data SIMD ; Digital Signalprocessor DSP ; Applikation-Specifik Integrerad Krets ASIC ; Maskininlärning; Djupinlärning; Konvolutionella Neurala Nätverk CNN ; Very Long Instruction Word VLIW ; Single Instruction Multiple Data SIMD ;

   Sammanfattning : Machine learning is rapidly being integrated into all areas of society, however, that puts a lot of pressure on resource costraint hardware such as embedded systems. The company Ericsson is gradually integrating machine learning based on neural networks, so-called deep learning, into their radio products. LÄS MER

7. 5. Diffuser: Packet Spraying While Maintaining Order : Distributed Event Scheduler for Maintaining Packet Order while Packet Spraying in DPDK

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

   Författare :Vignesh Purushotham Srinivas; [2023]
   Nyckelord :Packet scheduling; Scheduling; Out of order; Data plane development kit; Parallel processing; Network processor; Paketschemaläggning; Schemaläggning; oordning; Dataplansutvecklingskit; Parallell bearbetning; Nätverksprocessor;

   Sammanfattning : The demand for high-speed networking applications has made Network Processors (NPs) and Central Computing Units (CPUs) increasingly parallel and complex, containing numerous on-chip processing cores. This parallelism can only be exploited fully by the underlying packet scheduler by efficiently utilizing all the available cores. LÄS MER