Master Degree / Yüksek Lisans Tezleri
Permanent URI for this collectionhttps://hdl.handle.net/11147/3008
Browse
3 results
Search Results
Now showing 1 - 3 of 3
Master Thesis Hardware acceleration with fpga Based electronic boards for machine learning(01. Izmir Institute of Technology, 2024) Akkuş, Batuhan; Gümüş, Abdurrahman; Apaydın, Mehmet SerkanSon yıllardaki makine ög˘renmesi algoritmalarındaki gelis¸meler uç cihazlardaki kullanımını da arttırmıs¸tır (Merenda et al., 2020). Makine ög˘renimi algoritmaları genel- likle GPU tabanlı bilgisayarlarda çalıs¸tırılmaktadır, bu da yüksek enerji tüketimi (De- sislavov et al., 2021), yog˘un donanım kaynag˘ı gereksinimleri ve büyük fiziksel boyutları (Liu et al., 2022) nedeniyle uç cihazlar için uygun olmamaktadır. Bu tez, donanım hızlandırıcısı olarak FPGA platformlarında makine ög˘renmesi algoritmalarının, özellikle derin sinir ag˘larının uygulanması ve çıkarım yapılmasını aras¸tırarak, düs¸ük güç tüke- timi, verimli donanım kullanımı ve yüksek çıkarım performansı elde etmeyi hedefle- mektedir. Bu sistemlerin uç cihazlara adaptasyonu için esneklig˘i ve verimlilig˘i artırmak amacıyla, CNV ag˘ının (Umuroglu et al., 2017b) daha hafif bir varyasyonu olan CNV light gelis¸tirilmis¸. Bu ag˘, PyTorch tabanlı bir araç olan Brevitas (Pappalardo et al., 2019) ile nicemleme-farkında-eg˘itim yöntemi, kullanılarak 1, 2, 4 ve 8-bit seviyelerine nicemleme yapılmıs¸tır. CNV light ag˘ı CIFAR-10, SVHN, GTSRB ve MNIST veri setleri üzerinde Brevitas ile eg˘itilmis¸tir. Modeller FINN çerçevesi (Umuroglu et al., 2017a) kullanılarak FPGA'ya sentezlenmis¸tir. Modeller en fazla, en az ve sabit FPS seviye donanım kul- lanımına göre ayarlanmıs¸tır. Xilinx XC7Z020-1CLG400C FPGA, modelin metriklerini deg˘erlendirmek ve raporlamak için kullanılmıs¸tır. GTSRB veri setinde, ikili (W1A1) nicemleme yapılmıs¸ CNV light ag˘ı, tüm donanım kullanımları için %95.12 dog˘ruluk ve en fazla donanım kullanımında 12,191 FPS performansı ve 3.20W güç tüketimi elde etti, minimum donanım kullanımı için ise 6 FPS ve 1.62W güç tüketti. Sonuçlar, FPGA'ların uç cihazlarda makine ög˘renmesi modellerini verimli ve ölçeklenebilir platformlar olarak kullanılabileceg˘ini göstermektedir.Master Thesis Machine Learning Based Resource Allocation for Massive Mimo Systems(01. Izmir Institute of Technology, 2023) Sevgi, Hüseyin Can; Özbek, BernaCell-free massive MIMO communication systems is a promising technology that uses access-points(APs) deployed throughout the coverage area instead of usual cellular systems with centralized BS to serve multiple users simultaneously. By exploiting the large number of antennas and adopting advanced signal processing techniques, cell-free massive MIMO can mitigate inter-user interference and enhance the overall system performance. Optimal power allocation plays a crucial role in maximizing the spectral and energy efficiency of wireless networks. By intelligently allocating transmit power to different users, a balance between maximizing the system throughput and minimizing the total energy consumption can be achieved. In addition, user-centric clustering(UCC) is also a key technique to improve the performance of cell-free massive MIMO systems. This technique aims to pair user equipments (UEs) with appropriate APs to facilitate efficient resource allocation and interference management. In this thesis, cell-free mMIMO communication system is investigated through user-centric clustering and power allocation. The power allocation optimization problem is formulated to maximize energy efficiency of cell-free mMIMO systems and solved by using interior-point algorithm. User-centric clustering algorithm is proposed by disabling the non-master APs that are serving only one user. This additional feature aims to reduce total power consumption of the system without sacrificing the advantages of the cell-free mMIMO communication systems. Additionally, we propose a machine learning(ML) approach to reduce the computation time required for power allocation optimization. Through extensive simulations, we demonstrate the effectiveness of the proposed algorithms in achieving significant gains in spectral and energy efficiency in cell-free massive MIMO systems. The results highlight the importance of optimal power allocation and user-centric clustering to design an efficient cell-free mMIMO systems through machine learning approach.Master Thesis Quasi-Supervised Strategies for Compound-Protein Interaction Prediction [master Thesis](01. Izmir Institute of Technology, 2021) Çakı, Onur; Karaçalı, BilgeIn-silico prediction of compound-protein interaction using computational methods preserves its importance in various pharmacology applications because the wet-lab experiments are time-consuming, laborious and costly. Most machine learning methods proposed to that end approach this problem with supervised learning strategies in which known interactions are labeled as positive and the rest are labeled as negative. However, treating all unknown interactions as negative instances may lead to inaccuracies in real practice since some of the unknown interactions are bound to be positive interactions waiting to be identified as such. In this study, we propose to address this problem using the Quasi-Supervised Learning algorithm. In this framework, potential interactions are predicted by estimating the overlap between two datasets: a true positive dataset which consists of compound-protein pairs with known interactions and an unknown dataset which consists of all the remaining compound-protein pairs. The potential interactions are then identified as those in the unknown dataset that overlap with the interacting pairs in the true positive dataset in terms of the associated similarity structure between interacting pairs. Experimental results on GPCR and Nuclear Receptor datasets show that the proposed method can identify actual interactions from all possible combinations.