Low power motion estimation hardware designs

Motion Estimation (ME) is the most computationally intensive and most power consuming part of video compression and video enhancement systems. ME is used in video compression standards such as H.264/MPEG-4 and it is used in video enhancement algorithms such as frame rate conversion and de-interlacing. Half pixel (HP) ME increases the video coding efficiency at the expense of increased computational complexity. Therefore, in this thesis, we designed and implemented efficient integer pixel (IP) ME hardware implementing full search ME algorithm, and we proposed techniques for reducing the dynamic power consumptions of IP and HP ME hardware. The proposed ME hardware architectures are implemented in Verilog HDL and mapped to Xilinx FPGAs. The FPGA implementations are verified with post place & route simulations. We proposed comparison prediction (CP) technique for reducing the power consumption of IP block matching (BM) ME hardware. CP technique reduces the power consumption of absolute difference operations performed by IP BM ME hardware. The proposed technique can easily be used in all IP BM ME hardware. It reduced the power consumption of a fixed block size IP BM ME hardware implementing full search algorithm by 9.3% with 0.04% PSNR loss on a Xilinx XC2VP30-7 FPGA. We also proposed two techniques for reducing the power consumption of H.264 HP ME hardware. The first technique is vector dependent sum of absolute difference (SAD) reuse which reduces the amount of computations for variable block size H.264 HP ME with no PSNR loss. The second technique is a novel modification of the HP search algorithm which adaptively tries to use the IP motion vector trajectories to reduce HP search to 1-D. This technique causes an average PSNR loss of 0.36 dB. The two techniques reduced the power consumption of a variable block size H.264 HP ME hardware by 6% and 31% on a Xilinx Virtex 6 FPGA respectively

Efficient hardware implementations of high throughput SHA-3 candidates keccak, luffa and blue midnight wish for single- and multi-message hashing

In November 2007 NIST announced that it would organize the SHA-3 competition to select a new cryptographic hash function family by 2012. In the selection process, hardware performances of the candidates will play an important role. Our analysis of previously proposed hardware implementations shows that three SHA-3 candidate algorithms can provide superior performance in hardware: Keccak, Luffa and Blue Midnight Wish (BMW). In this paper, we provide efficient and fast hardware implementations of these three algorithms. Considering both single- and multi-message hashing applications with an emphasis on both speed and efficiency, our work presents more comprehensive analysis of their hardware performances by providing different performance figures for different target devices. To our best knowledge, this is the first work that provides a comparative analysis of SHA-3 candidates in multi-message applications. We discover that BMW algorithm can provide much higher throughput than previously reported if used in multi-message hashing. We also show that better utilization of resources can increase speed via different configurations. We implement our designs using Verilog HDL, and map to both ASIC and FPGA devices (Spartan3, Virtex2, and Virtex 4) to give a better comparison with those in the literature. We report total area, maximum frequency, maximum throughput and throughput/area of the designs for all target devices. Given that the selection process for SHA3 is still open; our results will be instrumental to evaluate the hardware performance of the candidates

A reconfigurable H.264 video encoder hardware (Bir uyarlanır H.264 video kodlayıcı donanımı)

Motion Estimation (ME) is the most computationally intensive part of video compression systems. Multiple reference frame (MRF) ME used in H.264 standard increases the video coding efficiency at the expense of increased computational complexity and power consumption. Therefore, in this paper, we present a reconfigurable baseline H.264 video encoder hardware in which the number of reference frames used for MRF ME can be configured based on the application requirements in order to trade-off video coding efficiency and power consumption. The proposed H.264 video encoder hardware is based on an existing low cost H.264 intra frame coder hardware and it includes new reconfigurable MRF ME, mode decision and motion compensation hardware. The proposed H.264 video encoder hardware is capable of processing 55 CIF (352×288) frames per second and its power consumption ranges between 115 mW and 235 mW depending on the number of reference frames used for MRF ME

A low power adaptive H.264 video encoder hardware

In this paper, we propose a low power adaptive baseline H.264 video encoder hardware for portable consumer electronics devices. Multiple reference frame motion estimation (MRF ME) used in H.264 standard increases the video coding efficiency at the expense of increased computational complexity and power consumption. In the proposed hardware, the number of reference frames used for MRF ME can be dynamically changed for each macroblock in order to trade-off video coding efficiency and power consumption. The proposed hardware can code 55 CIF (352x288) frames per second with low hardware cost. Its power consumption ranges between 115mW and 235mW depending on the number of reference frames used for MRF ME

Low power techniques for motion estimation hardware

Motion estimation (ME) is the most computationally intensive and the most power consuming part of video compression and video enhancement systems. In this paper, we propose a novel power reduction technique for ME hardware. We quantified the impact of glitch reduction, clock gating and the proposed technique on the power consumption of two full search ME hardware implementations on a Xilinx Virtex II FPGA using Xilinx XPower tool. Glitch reduction and clock gating together achieved an average of 21% dynamic power reduction. The proposed technique achieved an average of 23% dynamic power reduction with an average of 0.4 db PSNR loss. The proposed technique achieves better power reduction than pixel truncation technique with a similar PSNR loss

A novel power reduction technique for block matching motion estimation hardware

Motion Estimation (ME) is the most computationally intensive part of video compression and video enhancement systems. Therefore, in this paper, we propose comparison prediction (CP) technique for reducing the power consumption of block matching (BM) ME hardware. CP technique reduces the power consumption of absolute difference operations performed by BM ME hardware. CP technique can easily be used in all BM ME hardware. In this paper, we applied it to a 256 processing element fixed block size ME hardware implementing full search algorithm. It reduced the average dynamic power consumption of this ME hardware by 2.2% with no Peak Signal-to-Noise Ratio (PSNR) loss and by 9.3% with 0.04% PSNR loss on a XC2VP30-7 FPGA