IDMoB: IoT Data Marketplace on Blockchain

Today, Internet of Things (IoT) devices are the powerhouse of data generation with their ever-increasing numbers and widespread penetration. Similarly, artificial intelligence (AI) and machine learning (ML) solutions are getting integrated to all kinds of services, making products significantly more "smarter". The centerpiece of these technologies is "data". IoT device vendors should be able keep up with the increased throughput and come up with new business models. On the other hand, AI/ML solutions will produce better results if training data is diverse and plentiful. In this paper, we propose a blockchain-based, decentralized and trustless data marketplace where IoT device vendors and AI/ML solution providers may interact and collaborate. By facilitating a transparent data exchange platform, access to consented data will be democratized and the variety of services targeting end-users will increase. Proposed data marketplace is implemented as a smart contract on Ethereum blockchain and Swarm is used as the distributed storage platform.Comment: Presented at Crypto Valley Conference on Blockchain Technology (CVCBT 2018), 20-22 June 2018 - published version may diffe

Common Subexpression-based Compression and Multiplication of Sparse Constant Matrices

In deep learning inference, model parameters are pruned and quantized to reduce the model size. Compression methods and common subexpression (CSE) elimination algorithms are applied on sparse constant matrices to deploy the models on low-cost embedded devices. However, the state-of-the-art CSE elimination methods do not scale well for handling large matrices. They reach hours for extracting CSEs in a $200 \times 200$ matrix while their matrix multiplication algorithms execute longer than the conventional matrix multiplication methods. Besides, there exist no compression methods for matrices utilizing CSEs. As a remedy to this problem, a random search-based algorithm is proposed in this paper to extract CSEs in the column pairs of a constant matrix. It produces an adder tree for a $1000 \times 1000$ matrix in a minute. To compress the adder tree, this paper presents a compression format by extending the Compressed Sparse Row (CSR) to include CSEs. While compression rates of more than $50\%$ can be achieved compared to the original CSR format, simulations for a single-core embedded system show that the matrix multiplication execution time can be reduced by $20\%$

AxleDB: A novel programmable query processing platform on FPGA

With the rise of Big Data, providing high-performance query processing capabilities through the acceleration of the database analytic has gained significant attention. Leveraging Field Programmable Gate Array (FPGA) technology, this approach can lead to clear benefits. In this work, we present the design and implementation of AxleDB: An FPGA-based platform that enables fast query processing for database systems by melding novel database-specific accelerators with commercial-off-the-shelf (COTS) storage using modern interfaces, in a novel, unified, and a programmable environment. AxleDB can perform a large subset of SQL queries through its set of instructions that can map compute-intensive database operations, such as filter, arithmetic, aggregate, group by, table join, or sort, on to the specialized high-throughput accelerators. To minimize the amount of SSD I/O operations required, AxleDB also supports hardware MinMax indexing for databases. We evaluated AxleDB with five decision support queries from the TPC-H benchmark suite and achieved a speedup from 1.8X to 34.2X and energy efficiency from 2.8X to 62.1X, in comparison to the state-of-the-art DBMS, i.e., PostgreSQL and MonetDB.The research leading to these results has received funding from the European Union Seventh Framework Program (FP7) (under the AXLE project GA number 318633), the Ministry of Economy and Competitiveness of Spain (under contract number TIN2015-65316-p), Turkish Ministry of Development TAM Project (number 2007K120610), and Bogazici University Scientific Projects (number 7060).Peer ReviewedPostprint (author's final draft

KUVAYİ MİLLİYE’DE TÜRK HALKININ EMPERYALİZME DİRENİŞİ

Uluslararası Bakalorya Programı Bitirme tezi olarak hazırlanan bu çalışmada “Emperyalizm” ve “Vatan Sevgisi” konularının Kuvayi Milliye Destanı’nda şair tarafından nasıl ele alındığı araştırılmıştır. Bu iki öğenin, çalışmaya konu olan yapıtta Kurtuluş Savaşı sürecini nasıl etkilediği, yönlendirdiği ve ne gibi sonuçlar doğurduğu ayrıntılı olarak iredelenmiştir. Bu tezin yazılma amacı da, halkın olağanüstü fedakârlığıyla, vatanseverliğiyle ve cesaretiyle kazanılan bu büyük destansı zaferin ne denli koşullar altında ve ne uğruna kazanıldığının yazınsal gerçeklikle nasıl yansıtıldığını görmektir. Bu sorunun ve konunun araştırılması ve bu araştırma sonucunda ortaya çıkacak olan tüm bilgiler, Türk insanının ne denli uğraşlar altında bağımsızlığına kavuştuğunun, asla kaybedilmemesi ve sonsuza kadar korunması gereken “Tam Bağımsız Türkiye”nin nasıl kurulduğunun yazınsal gerçeklikte nasıl yansıtıldığını somutlayacaktır. Bu soru araştırılırken Nazım Hikmet’in Kuvayi Milliye Destanı adlı yapıtı esas alınmış ve yapıt ayrıntılı değerlendirilmiş, araştırma konusuns paralel dizelerden yararlanılmıştır. Araştırma sonucunda da Kurtuluş Savaşı’nın Tüm Türk Halkı’nın gösterdiği vatanseverlik ve özverinin sonucu kazanılmasının yazınsal gerçeklikte de temel oluşturduğu görülmüştür