We believe that by adapting architectures to fit the requirements of a given application domain, we can significantly improve the efficiency of computation. To validate the idea for our application domain, we evaluate a wide spectrum of commodity computing platforms to quantify the potential benefits of heterogeneity and customization for the domain-specific applications. In particular, we choose medical imaging as the application domain for investigation, and study the application performance and energy efficiency across a diverse set of commodity hardware platforms, such as general-purpose multi-core CPUs, massive parallel many-core GPUs, low-power mobile CPUs and fine-grain customizable FPGAs. This study leads to a number of interesting observations that can be used to guide further development of domain-specific architectures.

Face annotation makes it easy to share and manage digital photos and videos. While state-of-the-art face recognition algorithms can achieve high accuracy to support automatic face annotation, their implementations on an embedded platform cannot achieve real-time performance due to the demanding computational requirement. However, the availability of an embedded GPU in most smartphones offers the opportunity to use it as an accelerator for the face recognition task. In this demonstration, we show that, with acceleration achieved by the embedded low-power GPU, a real-time face annotation system could be realized on an existing off-the-shelf smartphone.

This paper presents an efficient VLSI architecture design of MPEG-4 shape coding, which is the key technology for supporting the content-based functionality of the MPEG-4 video standard. The real-time constraint of MPEG-4 shape coding leads to a heavy computational bottleneck on today's computer architectures. To overcome this problem, design analysis and optimization of MPEG-4 shape coding are addressed. By utilizing the RISC-based model, computational behaviors of the MPEG-4 shape coding tool are carefully examined and analyzed. The characteristic of a large amount of bit-level data processing and data transfer of MPEG-4 shape coding motivates the optimization of bit-level data operations. Applying data-flow optimization and data reuse techniques, bit-level computation-efficient architectures, such as data-dispatch-based binary-shaped motion estimation, the delay-line model, and configurable context-based arithmetic coding, are designed to accelerate bit-level processing. These hardware blocks are integrated and scheduled in a very efficient data flow to achieve real-time performance for MPEG-4 CPL2 (core profile level 2) specification at 23.5 MHz clock rate. The system architecture is implemented using Verilog HDL and synthesized with a 0.35 /spl mu/m four-layer CMOS standard library.