Tile-Level Parallelism For H.264/Avc Codec Using Parallel Domain Decomposition Algorithm On Shared Memory Architecture

Tema tesis ini adalah berdasarkan kepada penggunaan ciri-ciri model selari dalam fasa reka bentuk algoritma untuk mengurangkan kerumitan pengiraan dalam perbandingan dengan algoritma bersiri. Dengan menganggap bahawa seni bina selari membentuk majoriti pengiraan nod dalam peranti digital, cadangan bagi algoritma selari-inheren adalah sesuai. Dalam karya ini, proses atau pengenalan bebenang didaftar dalam satu formula matematik untuk mengurai domain satu, dua, dan domain tiga dimensi. Penyelesaian senario ruang dua dimensi seterusnya disesuaikan sebagai tahap baru keselarian untuk pengekodan piawaian H.264/AVC kerana kerumitan pengiraan yang lebih tinggi daripada pengekodan video ini berbanding dengan piawaian sebelumnya. Tahap baru keselarian untuk pengekod H.264 / AVC ini telah direka untuk mempertimbangkan beberapa metrik pengekodean video dan berorientasikan selari. Kaedah selari peringkat-jubin H.264/AVC yang dicadangkan dibandingkan dengan pendekatan selari tahap kepingan dan tahap blok makro. The theme of this thesis is based on the utilisation of features of the parallel model in the design phase of an algorithm in order to reduce the computational complexity in comparison with the serial algorithm. By assuming that parallel architectures are forming the vast majority of computing nodes in digital devises, proposing inherently-parallel algorithms are no more an overstatement. In this work, the process or thread identification is used in a mathematical formulation to decompose a one-, two, and a three-dimensional domain. Then, the solution of the scenario of two-dimensional space is further customized to serve as a new level of parallelism for the H.264/AVC coding standard due to the higher computational complexity of this video coding in comparison with previous standards. This new level of parallelism for the H.264/AVC encoder has been designed in a way to consider several video coding and parallel- oriented metrics. As a further step, the proposed tile-level parallel H.264/AVC is compared with the slice-level and the macroblock-level parallel approaches

Energy-Efficient Computing for Mobile Signal Processing

Mobile devices have rapidly proliferated, and deployment of handheld devices continues to increase at a spectacular rate. As today's devices not only support advanced signal processing of wireless communication data but also provide rich sets of applications, contemporary mobile computing requires both demanding computation and efficiency. Most mobile processors combine general-purpose processors, digital signal processors, and hardwired application-specific integrated circuits to satisfy their high-performance and low-power requirements. However, such a heterogeneous platform is inefficient in area, power and programmability. Improving the efficiency of programmable mobile systems is a critical challenge and an active area of computer systems research. SIMD (single instruction multiple data) architectures are very effective for data-level-parallelism intense algorithms in mobile signal processing. However, new characteristics of advanced wireless/multimedia algorithms require architectural re-evaluation to achieve better energy efficiency. Therefore, fourth generation wireless protocol and high definition mobile video algorithms are analyzed to enhance a wide-SIMD architecture. The key enhancements include 1) programmable crossbar to support complex data alignment, 2) SIMD partitioning to support fine-grain SIMD computation, and 3) fused operation to support accelerating frequently used instruction pairs. Near-threshold computation has been attractive in low-power architecture research because it balances performance and power. To further improve energy efficiency in mobile computing, near-threshold computation is applied to a wide SIMD architecture. This proposed near-threshold wide SIMD architecture-Diet SODA-presents interesting architectural design decisions such as 1) very wide SIMD datapath to compensate for degraded performance induced by near-threshold computation and 2) scatter-gather data prefetcher to exploit large latency gap between memory and the SIMD datapath. Although near-threshold computation provides excellent energy efficiency, it suffers from increased delay variations. A systematic study of delay variations in near-threshold computing is performed and simple techniques-structural duplication and voltage/frequency margining-are explored to tolerate and mitigate the delay variations in near-threshold wide SIMD architectures. This dissertation analyzes representative wireless/multimedia mobile signal processing algorithms, proposes an energy-efficient programmable platform, and evaluates performance and power. A main theme of this dissertation is that the performance and efficiency of programmable embedded systems can be significantly improved with a combination of parallel SIMD and near-threshold computations.Ph.D.Electrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/86356/1/swseo_1.pd

Variable block size motion estimation hardware for video encoders.

Li, Man Ho.Thesis submitted in: November 2006.Thesis (M.Phil.)--Chinese University of Hong Kong, 2007.Includes bibliographical references (leaves 137-143).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement --- p.ivChapter 1 --- Introduction --- p.1Chapter 1.1 --- Motivation --- p.3Chapter 1.2 --- The objectives of this thesis --- p.4Chapter 1.3 --- Contributions --- p.5Chapter 1.4 --- Thesis structure --- p.6Chapter 2 --- Digital video compression --- p.8Chapter 2.1 --- Introduction --- p.8Chapter 2.2 --- Fundamentals of lossy video compression --- p.9Chapter 2.2.1 --- Video compression and human visual systems --- p.10Chapter 2.2.2 --- Representation of color --- p.10Chapter 2.2.3 --- Sampling methods - frames and fields --- p.11Chapter 2.2.4 --- Compression methods --- p.11Chapter 2.2.5 --- Motion estimation --- p.12Chapter 2.2.6 --- Motion compensation --- p.13Chapter 2.2.7 --- Transform --- p.13Chapter 2.2.8 --- Quantization --- p.14Chapter 2.2.9 --- Entropy Encoding --- p.14Chapter 2.2.10 --- Intra-prediction unit --- p.14Chapter 2.2.11 --- Deblocking filter --- p.15Chapter 2.2.12 --- Complexity analysis of on different com- pression stages --- p.16Chapter 2.3 --- Motion estimation process --- p.16Chapter 2.3.1 --- Block-based matching method --- p.16Chapter 2.3.2 --- Motion estimation procedure --- p.18Chapter 2.3.3 --- Matching Criteria --- p.19Chapter 2.3.4 --- Motion vectors --- p.21Chapter 2.3.5 --- Quality judgment --- p.22Chapter 2.4 --- Block-based matching algorithms for motion estimation --- p.23Chapter 2.4.1 --- Full search (FS) --- p.23Chapter 2.4.2 --- Three-step search (TSS) --- p.24Chapter 2.4.3 --- Two-dimensional Logarithmic Search Algorithm (2D-log search) --- p.25Chapter 2.4.4 --- Diamond Search (DS) --- p.25Chapter 2.4.5 --- Fast full search (FFS) --- p.26Chapter 2.5 --- Complexity analysis of motion estimation --- p.27Chapter 2.5.1 --- Different searching algorithms --- p.28Chapter 2.5.2 --- Fixed-block size motion estimation --- p.28Chapter 2.5.3 --- Variable block size motion estimation --- p.29Chapter 2.5.4 --- Sub-pixel motion estimation --- p.30Chapter 2.5.5 --- Multi-reference frame motion estimation . --- p.30Chapter 2.6 --- Picture quality analysis --- p.31Chapter 2.7 --- Summary --- p.32Chapter 3 --- Arithmetic for video encoding --- p.33Chapter 3.1 --- Introduction --- p.33Chapter 3.2 --- Number systems --- p.34Chapter 3.2.1 --- Non-redundant Number System --- p.34Chapter 3.2.2 --- Redundant number system --- p.36Chapter 3.3 --- Addition/subtraction algorithm --- p.38Chapter 3.3.1 --- Non-redundant number addition --- p.39Chapter 3.3.2 --- Carry-save number addition --- p.39Chapter 3.3.3 --- Signed-digit number addition --- p.40Chapter 3.4 --- Bit-serial algorithms --- p.42Chapter 3.4.1 --- Least-significant-bit (LSB) first mode --- p.42Chapter 3.4.2 --- Most-significant-bit (MSB) first mode --- p.43Chapter 3.5 --- Absolute difference algorithm --- p.44Chapter 3.5.1 --- Non-redundant algorithm for absolute difference --- p.44Chapter 3.5.2 --- Redundant algorithm for absolute difference --- p.45Chapter 3.6 --- Multi-operand addition algorithm --- p.47Chapter 3.6.1 --- Bit-parallel non-redundant adder tree implementation --- p.47Chapter 3.6.2 --- Bit-parallel carry-save adder tree implementation --- p.49Chapter 3.6.3 --- Bit serial signed digit adder tree implementation --- p.49Chapter 3.7 --- Comparison algorithms --- p.50Chapter 3.7.1 --- Non-redundant comparison algorithm --- p.51Chapter 3.7.2 --- Signed-digit comparison algorithm --- p.52Chapter 3.8 --- Summary --- p.53Chapter 4 --- VLSI architectures for video encoding --- p.54Chapter 4.1 --- Introduction --- p.54Chapter 4.2 --- Implementation platform - (FPGA) --- p.55Chapter 4.2.1 --- Basic FPGA architecture --- p.55Chapter 4.2.2 --- DSP blocks in FPGA device --- p.56Chapter 4.2.3 --- Advantages employing FPGA --- p.57Chapter 4.2.4 --- Commercial FPGA Device --- p.58Chapter 4.3 --- Top level architecture of motion estimation processor --- p.59Chapter 4.4 --- Bit-parallel architectures for motion estimation --- p.60Chapter 4.4.1 --- Systolic arrays --- p.60Chapter 4.4.2 --- Mapping of a motion estimation algorithm onto systolic array --- p.61Chapter 4.4.3 --- 1-D systolic array architecture (LA-ID) --- p.63Chapter 4.4.4 --- 2-D systolic array architecture (LA-2D) --- p.64Chapter 4.4.5 --- 1-D Tree architecture (GA-1D) --- p.64Chapter 4.4.6 --- 2-D Tree architecture (GA-2D) --- p.65Chapter 4.4.7 --- Variable block size support in bit-parallel architectures --- p.66Chapter 4.5 --- Bit-serial motion estimation architecture --- p.68Chapter 4.5.1 --- Data Processing Direction --- p.68Chapter 4.5.2 --- Algorithm mapping and dataflow design . --- p.68Chapter 4.5.3 --- Early termination scheme --- p.69Chapter 4.5.4 --- Top-level architecture --- p.70Chapter 4.5.5 --- Non redundant positive number to signed digit conversion --- p.71Chapter 4.5.6 --- Signed-digit adder tree --- p.73Chapter 4.5.7 --- SAD merger --- p.74Chapter 4.5.8 --- Signed-digit comparator --- p.75Chapter 4.5.9 --- Early termination controller --- p.76Chapter 4.5.10 --- Data scheduling and timeline --- p.80Chapter 4.6 --- Decision metric in different architectural types . . --- p.80Chapter 4.6.1 --- Throughput --- p.81Chapter 4.6.2 --- Memory bandwidth --- p.83Chapter 4.6.3 --- Silicon area occupied and power consump- tion --- p.83Chapter 4.7 --- Architecture selection for different applications . . --- p.84Chapter 4.7.1 --- CIF and QCIF resolution --- p.84Chapter 4.7.2 --- SDTV resolution --- p.85Chapter 4.7.3 --- HDTV resolution --- p.85Chapter 4.8 --- Summary --- p.86Chapter 5 --- Results and comparison --- p.87Chapter 5.1 --- Introduction --- p.87Chapter 5.2 --- Implementation details --- p.87Chapter 5.2.1 --- Bit-parallel 1-D systolic array --- p.88Chapter 5.2.2 --- Bit-parallel 2-D systolic array --- p.89Chapter 5.2.3 --- Bit-parallel Tree architecture --- p.90Chapter 5.2.4 --- MSB-first bit-serial design --- p.91Chapter 5.3 --- Comparison between motion estimation architectures --- p.93Chapter 5.3.1 --- Throughput and latency --- p.93Chapter 5.3.2 --- Occupied resources --- p.94Chapter 5.3.3 --- Memory bandwidth --- p.95Chapter 5.3.4 --- Motion estimation algorithm --- p.95Chapter 5.3.5 --- Power consumption --- p.97Chapter 5.4 --- Comparison to ASIC and FPGA architectures in past literature --- p.99Chapter 5.5 --- Summary --- p.101Chapter 6 --- Conclusion --- p.102Chapter 6.1 --- Summary --- p.102Chapter 6.1.1 --- Algorithmic optimizations --- p.102Chapter 6.1.2 --- Architecture and arithmetic optimizations --- p.103Chapter 6.1.3 --- Implementation on a FPGA platform . . . --- p.104Chapter 6.2 --- Future work --- p.106Chapter A --- VHDL Sources --- p.108Chapter A.1 --- Online Full Adder --- p.108Chapter A.2 --- Online Signed Digit Full Adder --- p.109Chapter A.3 --- Online Pull Adder Tree --- p.110Chapter A.4 --- SAD merger --- p.112Chapter A.5 --- Signed digit adder tree stage (top) --- p.116Chapter A.6 --- Absolute element --- p.118Chapter A.7 --- Absolute stage (top) --- p.119Chapter A.8 --- Online comparator element --- p.120Chapter A.9 --- Comparator stage (top) --- p.122Chapter A.10 --- MSB-first motion estimation processor --- p.134Bibliography --- p.13

Architectures for Adaptive Low-Power Embedded Multimedia Systems

This Ph.D. thesis describes novel hardware/software architectures for adaptive low-power embedded multimedia systems. Novel techniques for run-time adaptive energy management are proposed, such that both HW & SW adapt together to react to the unpredictable scenarios. A complete power-aware H.264 video encoder was developed. Comparison with state-of-the-art demonstrates significant energy savings while meeting the performance constraint and keeping the video quality degradation unnoticeable

Rinnakkainen toteutus H.265 videokoodaus standardille

The objective of this study was to research the scalability of the parallel features in the new H.265 video compression standard, also know as High Efficiency Video Coding (HEVC). Compared to its predecessor, the H.264 standard, H.265 typically achieves around 50% bitrate reduction for the same subjective video quality. Especially videos with higher resolution (Full HD and beyond) achieve better compression ratios. Also a better utilization of parallel computing resources is provided. H.265 introduces two novel parallelization features: Tiles and Wavefront Parallel Processing (WPP). In Tiles, each video frame is divided into areas that can be decoded without referencing to other areas in the same frame. In WPP, the relations between code blocks in a frame are encoded so that the decoding process can progress through the frame as a front using multiple threads. In this study, the reference implementation for the H.265 decoder was augmented to support both of these parallelization features. The performance of the parallel implementations was measured using three different setups. From the measurement results it could be seen that the introduction of more CPU cores reduced the total decode time of the video frames to a certain point. When using the Tiles feature, it was observed that the encoding geometry, i.e. how each frame was divided into individually decodable areas, had a noticeable effect on the decode times with certain thread counts. When using WPP, it was observed that what was mostly synchronization overhead, sometimes had a negative effect on the decode times when using larger (4-12) amounts of threads.Tämän tutkimuksen aiheena oli tutkia uuden H.265 videonpakkausstandardin (tunnetaan myös nimellä HEVC (engl. High Efficiency Video Coding)) rinnakkaisuusominaisuuksien skaalautuvuutta. Verrattuna edeltäjäänsä, H.264 videonpakkaustandardiin, H.265 tyypillisesti saavuttaa samalla kuvanlaadulla noin 50% pienemmän pakkauskoon. Erityisesti suuren resoluution videoilla (Full HD ja suuremmat) pakkaustehokkuuden paremmuus korostuu. Huomiota on kiinnitetty myös moniydinprosessoreiden hyödyntämiseen videokoodauksessa. H.265 tarjoaa kaksi uutta rinnakkaisuusominaisuutta: niin kutsutut Tiles- ja WPP-menetelmät (engl. \emph{Wavefront Parallel Processing}). Tiles-menetelmässä jokainen videon kuva jaetaan alueisiin, jotka voidaan purkaa viittaamatta saman kuvan muihin alueisiin. WPP-menetelmässä suhteet kuvan lohkoihin pakataan siten että purkamisprosessi pystyy etenemään kuvan läpi rintamana hyödyntäen useampia säikeitä. Tässä tutkimuksessa H.265 videodekooderin referenssitoteutusta laajennettiin tukemaan molempia näistä rinnakkaisuusominaisuuksista. Suorituskykyä mitattiin käyttäen kolmea eri mittausasetelmaa. Mittaustuloksista ilmeni, että prosessoriydinten lukumäärän kasvattaminen nopeutti videoiden purkamista tiettyyn pisteeseen asti. Tiles-menetelmää mitatessa havaittiin, että alueiden geometrialla, eli kuinka kuva jaettiin riippumattomiin alueisiin, on huomattava vaikutus purkamisnopeuteen tietyillä säiemäärillä. WPP-menetelmää mitattaessa havaittiin että korkeampiin säiemääriin (4-12) siirryttäessä purkamisnopeus alkoi hidastua. Tämä johtui pääasiassa säikeiden keskinäiseen synkronointiin kuluvasta ajasta