Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/2358
Title: Ultra yüksek hızlı ve düşük enerjili yapay sinir hücre devresinin tasarımı ve gerçeklenmesi
Other Titles: Design and implementation of an ultra high speed and low energy artificial neuron
Authors: Bozbey, Ali
Karamüftüoğlu, Mustafa Altay
Keywords: Superconductor
Artificial neuron
Neuron circuit
Asynchronous threshold circuit
Integrate and fire model neuron
Leaky IFN
Süperiletken
Yapay nöron
Nöron devresi
Asenkron eşik devresi
Topla ve ateşle nöron modeli
Sızıntılı topla ve ateşle nöronu
Issue Date: 2018
Publisher: TOBB University of Economics and Technology,Graduate School of Engineering and Science
TOBB ETÜ Fen Bilimleri Enstitüsü
Source: Karamüftüoğlu, M. (2018). Ultra yüksek hızlı ve düşük enerjili yapay sinir hücre devresinin tasarımı ve gerçeklenmesi. Ankara: TOBB ETÜ Fen Bilimleri Enstitüsü. [Yayınlanmamış yüksek lisans tezi]
Abstract: Etkili hesaplama işlemlerinde kullanılmak amacıyla insan beyin fonksiyonlarının ve genel prensiplerinin yapay nöronlar aracılığıyla nasıl taklit edileceğinin anlaşılması, mevcut bilim topluluğunu etkilemiştir. İşlevsellik, sinir hücreleri veya nöronlar olarak bilinen beyin hücrelerinin kendiliğinden birleşmesinden gelmektedir. İnsan beyin hücresinin moleküler düzeyde modellenmesi, biyolojik karmaşıklığı nedeniyle pratik değildir. Matematiksel yaklaşımlar ve teknolojik gelişmeler, yapay nöron modellerinin donanım ve yazılım uygulamasını kolaylaştırmaktadır. Sayısal yazılım araçları, yapay sinir ağlarında (YSA) biyolojik sinir ağı davranışını benimsemek için yapay sinirleri birbirine bağlamaktadır. YSA yazılım araçları, YSA'ların öğrenme becerileri, hesaplama gücü ve paralel işlem yoluyla yüksek hesaplama hızının olması nedeniyle sinir ağı uygulamalarının kullanımında geniş çapta kabul görmektedir. Ayrıca, YSA modelleri geleneksel hesaplama cihazlarından daha basittir. Yüksek performanslı sayısal işlem devrelerinde, bir nöron hücresi karmaşık problemlerin çözülebilme imkanlarını geliştirmektedir. Bu nedenle, temel bir nöron modeli, çip üzerinde bir YSA veya hibrit dijital devre oluşturma kapasitesine sahiptir. Bu çalışmada, biyolojik beyin hücresini taklit etmek için, çip üzerinde YSA oluşturma ve sızıntılı Topla ve Ateşle Nöron (Integrate and Fire Neuron, IFN) modelini sağlama potansiyeli olan bir Josephson Eklemi (Josephson Junction, JJ) tabanlı Yapay Nöron (Josephson Junction based Artificial Neuron, JJ-AN) devresi sunulmaktadır. Tasarlanan yapay nöron devresi, üç ana yapıdan oluşur: bir direnç tarafından kesintiye uğratılmış İki Eklemli Süperiletken Kuantum Girişim Aygıtı (Superconducting Quantum Interference Device, SQUID) yapısı (eşik döngüsü), seri direnç ve indüktans yapısı (sönümlenme döngüsü) ve eşik döngü ile sönümlenme döngü indüktansları arasındaki karşılıklı indüklenme. Sunulan model, sadece bir giriş ve bir çıkış portuna sahiptir ve bu yapı, devreyi nispeten basit olarak tanımlamaktadır. Bununla birlikte, nöron devresi, diğer nöron devreleriyle birlikte kullanılmasının yanı sıra Tek Akı Kuantum (Single Flux Quantum, SFQ) dijital kütüphane devreleriyle de bir araya getirildiği için böyle bir tasarımın optimizasyonu çok önemli bir süreçtir. Nöron modelinin çalışma frekansı 120 GHz'ye kadar gözlemlenmiştir. Araştırma için bir örnek olarak, farklı eşik değerleri oluşturan iki parametre seti, minimum çalışma aralığı sırasıyla %±23 ve %±7, eniyileyici tarafından ayarlanıp oluşturulmuştur.
The current scientific community captivated by understanding the general principles of human brain functions, as a further matter, on how to mimic the abilities by utilizing artificial neurons for more efficient computing. Functionality comes from self-assembly of brain cells, known as nerve cells or neurons. Modeling human brain cell at a molecular level is not practical on account of its biological complexity. Mathematical approaches and technological developments led the hardware and software implementation of artificial neuron models easier. Computational software tools connect artificial neurons to each other to create Artificial Neural Network (ANN) to adopt biological neural network behavior. ANN software tools have gained extensive acceptance for wide range use of neural network applications because of learning abilities, computational power and speed through parallel processing. Furthermore, the models of ANN are simpler than conventional computing devices. For high performance computing circuits, a neuron cell can enhance the possibilities of solving complex problems. Therefore, a basic neuron model has the capacity of building an ANN on chip or hybrid digital circuits. vii To mimic the biological brain cell, this study shows a Josephson Junction (JJ) based Artificial Neuron (JJ-AN) circuit that satisfies the capability of creating ANN on chip and leaky Integrate and Fire Neuron (IFN) model. This artificial neuron circuit is formed by three main structures: a double-junction SQUID interfered with a resistor (threshold loop), adjoined resistor and inductance structure (decaying loop), and mutual conductance between threshold loop and decaying loop inductances. The proposed model has only one input and one output ports and it makes the circuit relatively simple. Nevertheless, optimization of such a design is a crucial process as neuron circuit is not only used together with other neuron circuits but also combined all together with Single Flux Quantum (SFQ) digital library circuits. Operation frequency of neuron model is observed up to 120 GHz. As an example for the research, two parameter sets that make different threshold values are converged by the modified optimizer that shows minimum margins of ±23% and ±7% respectively. The current scientific community captivated by understanding the general principles of human brain functions, as a further matter, on how to mimic the abilities by utilizing artificial neurons for more efficient computing. Functionality comes from self-assembly of brain cells, known as nerve cells or neurons. Modeling human brain cell at a molecular level is not practical on account of its biological complexity. Mathematical approaches and technological developments led the hardware and software implementation of artificial neuron models easier. Computational software tools connect artificial neurons to each other to create Artificial Neural Network (ANN) to adopt biological neural network behavior. ANN software tools have gained extensive acceptance for wide range use of neural network applications because of learning abilities, computational power and speed through parallel processing. Furthermore, the models of ANN are simpler than conventional computing devices. For high performance computing circuits, a neuron cell can enhance the possibilities of solving complex problems. Therefore, a basic neuron model has the capacity of building an ANN on chip or hybrid digital circuits. vii To mimic the biological brain cell, this study shows a Josephson Junction (JJ) based Artificial Neuron (JJ-AN) circuit that satisfies the capability of creating ANN on chip and leaky Integrate and Fire Neuron (IFN) model. This artificial neuron circuit is formed by three main structures: a double-junction SQUID interfered with a resistor (threshold loop), adjoined resistor and inductance structure (decaying loop), and mutual conductance between threshold loop and decaying loop inductances. The proposed model has only one input and one output ports and it makes the circuit relatively simple. Nevertheless, optimization of such a design is a crucial process as neuron circuit is not only used together with other neuron circuits but also combined all together with Single Flux Quantum (SFQ) digital library circuits. Operation frequency of neuron model is observed up to 120 GHz. As an example for the research, two parameter sets that make different threshold values are converged by the modified optimizer that shows minimum margins of ±23% and ±7% respectively. The current scientific community captivated by understanding the general principles of human brain functions, as a further matter, on how to mimic the abilities by utilizing artificial neurons for more efficient computing. Functionality comes from self-assembly of brain cells, known as nerve cells or neurons. Modeling human brain cell at a molecular level is not practical on account of its biological complexity. Mathematical approaches and technological developments led the hardware and software implementation of artificial neuron models easier. Computational software tools connect artificial neurons to each other to create Artificial Neural Network (ANN) to adopt biological neural network behavior. ANN software tools have gained extensive acceptance for wide range use of neural network applications because of learning abilities, computational power and speed through parallel processing. Furthermore, the models of ANN are simpler than conventional computing devices. For high performance computing circuits, a neuron cell can enhance the possibilities of solving complex problems. Therefore, a basic neuron model has the capacity of building an ANN on chip or hybrid digital circuits. vii To mimic the biological brain cell, this study shows a Josephson Junction (JJ) based Artificial Neuron (JJ-AN) circuit that satisfies the capability of creating ANN on chip and leaky Integrate and Fire Neuron (IFN) model. This artificial neuron circuit is formed by three main structures: a double-junction SQUID interfered with a resistor (threshold loop), adjoined resistor and inductance structure (decaying loop), and mutual conductance between threshold loop and decaying loop inductances. The proposed model has only one input and one output ports and it makes the circuit relatively simple. Nevertheless, optimization of such a design is a crucial process as neuron circuit is not only used together with other neuron circuits but also combined all together with Single Flux Quantum (SFQ) digital library circuits. Operation frequency of neuron model is observed up to 120 GHz. As an example for the research, two parameter sets that make different threshold values are converged by the modified optimizer that shows minimum margins of ±23% and ±7% respectively.
URI: https://hdl.handle.net/20.500.11851/2358
https://tez.yok.gov.tr/UlusalTezMerkezi/tezSorguSonucYeni.jsp
Appears in Collections:Elektrik-Elektronik Mühendisliği Yüksek Lisans Tezleri / Electrical & Electronics Engineering Master Theses

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