VLSI Design of a Fast Pipelined 8x8 Discrete Cosine Transform

This paper presents a Very Large Scale Integrated (VLSI) design and implementation of a fixed-point 8x8 multiplierless Discrete Cosine Transform (DCT) using the ISO/IEC 23002-2 algorithm. The standard DCT algorithm, which is mainly used in image and video compression technology, consists of only adders, subtractors, and shifters, therefore making it efficient for hardware implementation. The VLSI implementation of the algorithm given in this paper further enhances the performance of the transform unit. Furthermore, circuit pipelining has been applied to the base design of the DCT, which significantly improves the performance by reducing the longest path in the non-pipeline design. The DCT has been implemented using semi-custom VLSI design methodology using the TSMC 0.13um process technology. Results show that our DCT designs can run up to around 1.7 Giga pixels/s, which is well above the timing required for real-time ultra-high definition 8K video

HEVC 2D-DCT architectures comparison for FPGA and ASIC implementations

This paper compares ASIC and FPGA implementations of two commonly used architectures for 2-dimensional discrete cosine transform (DCT), the parallel and folded architectures. The DCT has been designed for sizes 4x4, 8x8, and 16x16, and implemented on Silterra 180nm ASIC and Xilinx Kintex Ultrascale FPGA. The objective is to determine suitable low energy architectures to be used as their characteristics greatly differ in terms of cells usage, placement and routing methods on these platforms. The parallel and folded DCT architectures for all three sizes have been designed using Verilog HDL, including the basic serializer-deserializer input and output. Results show that for large size transform of 16x16, ASIC parallel architecture results in roughly 30% less energy compared to folded architecture. As for FPGAs, folded architecture results in roughly 34% less energy compared to parallel architecture. In terms of overall energy consumption between 180nm ASIC and Xilinx Ultrascale, ASIC implementation results in about 58% less energy compared to the FPGA

Low-power and application-specific SRAM design for energy-efficient motion estimation

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 181-189).Video content is expected to account for 70% of total mobile data traffic in 2015. High efficiency video coding, in this context, is crucial for lowering the transmission and storage costs for portable electronics. However, modern video coding standards impose a large hardware complexity. Hence, energy-efficiency of these hardware blocks is becoming more critical than ever before for mobile devices. SRAMs are critical components in almost all SoCs affecting the overall energy-efficiency. This thesis focuses on algorithm and architecture development as well as low-power and application-specific SRAM design targeting motion estimation. First, a motion estimation design is considered for the next generation video standard, HEVC. Hardware cost and coding efficiency trade-offs are quantified and an optimum design choice between hardware complexity and coding efficiency is proposed. Hardware-efficient search algorithm, shared search range across CU engines and pixel pre-fetching algorithms provide 4.3x area, 56x on-chip bandwidth and 151 x off-chip bandwidth reduction. Second, a highly-parallel motion estimation design targeting ultra-low voltage operation and supporting AVC/H.264 and VC-1 standards are considered. Hardware reconfigurability along with frame and macro-block parallel processing are implemented for this engine to maximize hardware sharing between multiple standards and to meet throughput constraints. Third, in the context of low-power SRAMs, a 6T and an 8T SRAM are designed in 28nm and 45nm CMOS technologies targeting low voltage operation. The 6T design achieves operation down to 0.6V and the 8T design achieves operation down to 0.5V providing ~ 2.8x and ~ 4.8x reduction in energy/access respectively. Finally, an application-specific SRAM design targeted for motion estimation is developed. Utilizing the correlation of pixel data to reduce bit-line switching activity, this SRAM achieves up to 1.9x energy savings compared to a similar conventional 8T design. These savings demonstrate that application-specific SRAM design can introduce a new dimension and can be combined with voltage scaling to maximize energy-efficiency.by Mahmut Ersin Sinangil.Ph.D

Approximate and timing-speculative hardware design for high-performance and energy-efficient video processing

Since the end of transistor scaling in 2-D appeared on the horizon, innovative circuit design paradigms have been on the rise to go beyond the well-established and ultraconservative exact computing. Many compute-intensive applications – such as video processing – exhibit an intrinsic error resilience and do not necessarily require perfect accuracy in their numerical operations. Approximate computing (AxC) is emerging as a design alternative to improve the performance and energy-efficiency requirements for many applications by trading its intrinsic error tolerance with algorithm and circuit efficiency. Exact computing also imposes a worst-case timing to the conventional design of hardware accelerators to ensure reliability, leading to an efficiency loss. Conversely, the timing-speculative (TS) hardware design paradigm allows increasing the frequency or decreasing the voltage beyond the limits determined by static timing analysis (STA), thereby narrowing pessimistic safety margins that conventional design methods implement to prevent hardware timing errors. Timing errors should be evaluated by an accurate gate-level simulation, but a significant gap remains: How these timing errors propagate from the underlying hardware all the way up to the entire algorithm behavior, where they just may degrade the performance and quality of service of the application at stake? This thesis tackles this issue by developing and demonstrating a cross-layer framework capable of performing investigations of both AxC (i.e., from approximate arithmetic operators, approximate synthesis, gate-level pruning) and TS hardware design (i.e., from voltage over-scaling, frequency over-clocking, temperature rising, and device aging). The cross-layer framework can simulate both timing errors and logic errors at the gate-level by crossing them dynamically, linking the hardware result with the algorithm-level, and vice versa during the evolution of the application’s runtime. Existing frameworks perform investigations of AxC and TS techniques at circuit-level (i.e., at the output of the accelerator) agnostic to the ultimate impact at the application level (i.e., where the impact is truly manifested), leading to less optimization. Unlike state of the art, the framework proposed offers a holistic approach to assessing the tradeoff of AxC and TS techniques at the application-level. This framework maximizes energy efficiency and performance by identifying the maximum approximation levels at the application level to fulfill the required good enough quality. This thesis evaluates the framework with an 8-way SAD (Sum of Absolute Differences) hardware accelerator operating into an HEVC encoder as a case study. Application-level results showed that the SAD based on the approximate adders achieve savings of up to 45% of energy/operation with an increase of only 1.9% in BD-BR. On the other hand, VOS (Voltage Over-Scaling) applied to the SAD generates savings of up to 16.5% in energy/operation with around 6% of increase in BD-BR. The framework also reveals that the boost of about 6.96% (at 50°) to 17.41% (at 75° with 10- Y aging) in the maximum clock frequency achieved with TS hardware design is totally lost by the processing overhead from 8.06% to 46.96% when choosing an unreliable algorithm to the blocking match algorithm (BMA). We also show that the overhead can be avoided by adopting a reliable BMA. This thesis also shows approximate DTT (Discrete Tchebichef Transform) hardware proposals by exploring a transform matrix approximation, truncation and pruning. The results show that the approximate DTT hardware proposal increases the maximum frequency up to 64%, minimizes the circuit area in up to 43.6%, and saves up to 65.4% in power dissipation. The DTT proposal mapped for FPGA shows an increase of up to 58.9% on the maximum frequency and savings of about 28.7% and 32.2% on slices and dynamic power, respectively compared with stat

Circuit implementations for high-efficiency video coding tools

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 71-72).High-Efficiency Video Coding (HEVC) is planned to be the successor video standard to the popular Advanced Video Coding (H.264/AVC) with a targeted 2x improvement in compression at the same quality. This improvement comes at the cost of increased complexity through the addition of new coding tools and increased computation in existing tools. The ever-increasing demand for higher resolution video further adds to the computation cost. In this work, digital circuits for two HEVC tools - inverse transform and deblocking filter are implemented to support Quad-Full HD (4K x 2K) video decoding at 30fps. Techniques to reduce power and area cost are investigated and synthesis results in 40nm CMOS technology and Virtex-6 FPGA platform are presented.by Mehul Tikekar.S.M

VLSI design concepts for iterative algorithms

Circuit design becomes more and more complicated, especially when the Very Large Scale Integration (VLSI) manufacturing technology node keeps shrinking down to nanoscale level. New challenges come up such as an increasing gap between the design productivity and the Moore’s Law. Leakage power becomes a major factor of the power consumption and traditional shared bus transmission is the critical bottleneck in the billion transistors Multi-Processor System–on–Chip (MPSoC) designs. These issues lead us to discuss the impact on the design of iterative algorithms. This thesis presents several strategies that satisfy various design con- straints, which can be used to explore superior solutions for the circuit design of iterative algorithms. Four selected examples of iterative al- gorithms are elaborated in this respect: hardware implementation of COordinate Rotation DIgital Computer (CORDIC) processor for sig- nal processing, configurable DCT and integer transformations based CORDIC algorithm for image/video compression, parallel Jacobi Eigen- value Decomposition (EVD) method with arbitrary iterations for com- munication, and acceleration of parallel Sparse Matrix–Vector Multipli- cation (SMVM) operations based Network–on–Chip (NoC) for solving systems of linear equations. These four applications of iterative meth- ods have been chosen since they cover a wide area of current signal processing tasks. Each method has its own unique design criteria when it comes to the direct implementation on the circuit level. Therefore, a balanced solution between various design tradeoffs is elaborated for each method. These tradeoffs are between throughput and power consumption, com- putational complexity and transformation accuracy, the number of in- ner/outer iterations and energy consumption, data structure and net- work topology. It is shown that all of these algorithms can be imple- mented on FPGA devices or as ASICs efficiently

Towards Computational Efficiency of Next Generation Multimedia Systems

To address throughput demands of complex applications (like Multimedia), a next-generation system designer needs to co-design and co-optimize the hardware and software layers. Hardware/software knobs must be tuned in synergy to increase the throughput efficiency. This thesis provides such algorithmic and architectural solutions, while considering the new technology challenges (power-cap and memory aging). The goal is to maximize the throughput efficiency, under timing- and hardware-constraints

Applications in Electronics Pervading Industry, Environment and Society

This book features the manuscripts accepted for the Special Issue “Applications in Electronics Pervading Industry, Environment and Society—Sensing Systems and Pervasive Intelligence” of the MDPI journal Sensors. Most of the papers come from a selection of the best papers of the 2019 edition of the “Applications in Electronics Pervading Industry, Environment and Society” (APPLEPIES) Conference, which was held in November 2019. All these papers have been significantly enhanced with novel experimental results. The papers give an overview of the trends in research and development activities concerning the pervasive application of electronics in industry, the environment, and society. The focus of these papers is on cyber physical systems (CPS), with research proposals for new sensor acquisition and ADC (analog to digital converter) methods, high-speed communication systems, cybersecurity, big data management, and data processing including emerging machine learning techniques. Physical implementation aspects are discussed as well as the trade-off found between functional performance and hardware/system costs