Sökning: "memory efficiency"

Visar resultat 1 - 5 av 202 uppsatser innehållade orden memory efficiency.

1. 1. EMONAS : Evolutionary Multi-objective Neuron Architecture Search of Deep Neural Network

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

   Författare :Jiayi Feng; [2023]
   Nyckelord :DNN Deep Neural Network ; NAS Neural Architecture Search ; EA Evolutionary Algorithm ; Multi-Objective Optimization; Binary One Optimization; Embedded Systems; DNN Deep Neural Network ; NAS Neural Architecture Search ; EA Evolutionary Algorithm ; Multi-Objective Optimization; Binary One Optimization; Inbyggda system;

   Sammanfattning : Customized Deep Neural Network (DNN) accelerators have been increasingly popular in various applications, from autonomous driving and natural language processing to healthcare and finance, etc. However, deploying them directly on embedded system peripherals within real-time operating systems (RTOS) is not easy due to the paradox of the complexity of DNNs and the simplicity of embedded system devices. LÄS MER

3. 2. Sales forecasting for supply chain using Artificial Intelligence

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

   Författare :Vaibhav Mittal; [2023]
   Nyckelord :AI; sales forecasting; supply chain; predictive analytics; AI; försäljningsprognoser; supply chain; predictiv analys;

   Sammanfattning : Supply chain management and logistics are two sectors currently experiencing a transformation thanks to the advent of AI(Artificial Intelligence) technologies. Leveraging predictive analytics powered by AI presents businesses with novel opportunities to streamline their operations effectively. LÄS MER

4. 3. A Conjugate Residual Solver with Kernel Fusion for massive MIMO Detection

   Master-uppsats, Högskolan i Halmstad/Centrum för forskning om tillämpade intelligenta system (CAISR)

   Författare :Ioannis Broumas; [2023]
   Nyckelord :MIMO; massive MIMO; GPU; CUDA; Software Defined Radio; SDR; MMSE; ZF; zero-forcing; parallel detection; iterative methods; conjugate residual; parallel computing; kernel fusion;

   Sammanfattning : This thesis presents a comparison of a GPU implementation of the Conjugate Residual method as a sequence of generic library kernels against implementations ofthe method with custom kernels to expose the performance gains of a keyoptimization strategy, kernel fusion, for memory-bound operations which is to makeefficient reuse of the processed data. For massive MIMO the iterative solver is to be employed at the linear detection stageto overcome the computational bottleneck of the matrix inversion required in theequalization process, which is 𝒪(𝑛3) for direct solvers. LÄS MER

5. 4. Comparison of Recommendation Systems for Auto-scaling in the Cloud Environment

   Master-uppsats, Blekinge Tekniska Högskola/Institutionen för datavetenskap

   Författare :Sai Nikhil Boyapati; [2023]
   Nyckelord :Auto-Scaling; Auto-Scaling Recommendations; Cloud Environment; K-Nearest Neighbors; Machine Learning; Recommendation Systems; Random Forests; Support Vector Machines;

   Sammanfattning : Background: Cloud computing’s rapid growth has highlighted the need for efficientresource allocation. While cloud platforms offer scalability and cost-effectiveness for a variety of applications, managing resources to match dynamic workloads remains a challenge. LÄS MER

7. 5. An Analysis of Data Cleaning Tools : A comparative analysis of the performance and effectiveness of data cleaning tools

   Kandidat-uppsats, Mittuniversitetet/Institutionen för data- och elektroteknik (2023-)

   Författare :Filip Stenegren; [2023]
   Nyckelord :Data Cleansing; Data Cleaning; Python; Excel; Regular-Expression; Datarensning; Datatvätt; Python; Excel; Regular-Expression;

   Sammanfattning : I en värld full av data är felaktiga eller inkonsekventa data oundvikliga, och datarensning, en process som rensar sådana skillnader, blir avgörande. Syftet med studien är att besvara frågan om vilka kriterier datarengöringsverktyg kan jämföras och utvärderas med. LÄS MER