Compare multimedia frameworks in mobile platforms

Multimedia feature is currently one of the most important features in mobile devices. Many modern mobile platforms use a centralized software stack to handle multimedia requirements that software stack is called multimedia framework. Multimedia framework belongs to the middleware layer of mobile operating system. It can be considered as a bridge that connects mobile operating system kernel, hardware drivers with UI applications. It supplies high level APIs that offers simple and easy solutions for complicated multimedia tasks to UI application developers. Multimedia Framework also manages and utilizes low lever system software and hardware in an efficient manner. It offers a centralize solution between high level demands and low level system resources. In this M.Sc. thesis project we have studied, analyzed and compared open source GStreamer, Android Stagefright and Microsoft Silverlight Media Framework from several perspectives. Some of the comparison perspectives are architecture, supported use cases, extensibility, implementation language and program language support (bindings), developer support, and legal status aspects. One of the main contributions of this thesis work is that clarifying in details the strength and weaknesses of each framework. Furthermore, the thesis should serve decision-making guidance when on needs to select a multimedia framework for a project. Moreover, and to enhance the impression with the three multimedia frameworks, a basic media player implementation is demonstrated with source code in the thesis.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format

Semiconductor Memory Applications in Radiation Environment, Hardware Security and Machine Learning System

abstract: Semiconductor memory is a key component of the computing systems. Beyond the conventional memory and data storage applications, in this dissertation, both mainstream and eNVM memory technologies are explored for radiation environment, hardware security system and machine learning applications. In the radiation environment, e.g. aerospace, the memory devices face different energetic particles. The strike of these energetic particles can generate electron-hole pairs (directly or indirectly) as they pass through the semiconductor device, resulting in photo-induced current, and may change the memory state. First, the trend of radiation effects of the mainstream memory technologies with technology node scaling is reviewed. Then, single event effects of the oxide based resistive switching random memory (RRAM), one of eNVM technologies, is investigated from the circuit-level to the system level. Physical Unclonable Function (PUF) has been widely investigated as a promising hardware security primitive, which employs the inherent randomness in a physical system (e.g. the intrinsic semiconductor manufacturing variability). In the dissertation, two RRAM-based PUF implementations are proposed for cryptographic key generation (weak PUF) and device authentication (strong PUF), respectively. The performance of the RRAM PUFs are evaluated with experiment and simulation. The impact of non-ideal circuit effects on the performance of the PUFs is also investigated and optimization strategies are proposed to solve the non-ideal effects. Besides, the security resistance against modeling and machine learning attacks is analyzed as well. Deep neural networks (DNNs) have shown remarkable improvements in various intelligent applications such as image classification, speech classification and object localization and detection. Increasing efforts have been devoted to develop hardware accelerators. In this dissertation, two types of compute-in-memory (CIM) based hardware accelerator designs with SRAM and eNVM technologies are proposed for two binary neural networks, i.e. hybrid BNN (HBNN) and XNOR-BNN, respectively, which are explored for the hardware resource-limited platforms, e.g. edge devices.. These designs feature with high the throughput, scalability, low latency and high energy efficiency. Finally, we have successfully taped-out and validated the proposed designs with SRAM technology in TSMC 65 nm. Overall, this dissertation paves the paths for memory technologies’ new applications towards the secure and energy-efficient artificial intelligence system.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

Vertical Optimizations of Convolutional Neural Networks for Embedded Systems

L'abstract è presente nell'allegato / the abstract is in the attachmen

Audiovisual preservation strategies, data models and value-chains

This is a report on preservation strategies, models and value-chains for digital file-based audiovisual content. The report includes: (a)current and emerging value-chains and business-models for audiovisual preservation;(b) a comparison of preservation strategies for audiovisual content including their strengths and weaknesses, and(c) a review of current preservation metadata models, and requirements for extension to support audiovisual files