Hidemasa Muta

Multilevel parallelization on the cell/B.E. for a motion JPEG 2000 encoding server

The Cell Broadband Engine (Cell/B.E.) is a novel multi-core microprocessor designed to provide high-performance processing capabilities for a wide range of applications. In this paper, we describe the worlds first JPEG 2000 and Motion JPEG 2000 encoder on Cell/B.E. Novel parallelization techniques for a Motion JPEG 2000 encoder that unleash the performance of the Cell/B.E. are proposed. Our Motion JPEG 2000 encoder consists of multiple video frame encoding servers on a cluster system for high-level parallelization. Each video frame encoding server runs on a heterogeneous multi-core Cell/B.E. processor, and utilizes its 8 Synergistic Processor Elements (SPEs) for low-level parallelization of the time consuming parts of the JPEG 2000 encoding process, such as the wavelet transform, the bit modeling, and the arithmetic coding. The effectiveness of high-level parallelization by the cluster system is also described, not only for the parallel encoding, but also for scalable performance improvement for real-time encoding and future enhancements. We developed all of the code from scratch for effective multilevel parallelization. Our results show that the Cell/B.E. is extremely efficient for this workload compared with commercially available processors, and thus we conclude that the Cell/B.E. is quite suitable for encoding next generation large pixel formats, such as 4K/2K-Digital Cinema.

Demonstrating the benefits of source-mask optimization and enabling technologies through experiment and simulations

In recent years the potential of Source-Mask Optimization (SMO) as an enabling technology for 22nm-and-beyond lithography has been explored and documented in the literature.1-5 It has been shown that intensive optimization of the fundamental degrees of freedom in the optical system allows for the creation of non-intuitive solutions in both the mask and the source, which leads to improved lithographic performance. These efforts have driven the need for improved controllability in illumination5-7 and have pushed the required optimization performance of mask design.8, 9 This paper will present recent experimental evidence of the performance advantage gained by intensive optimization, and enabling technologies like pixelated illumination. Controllable pixelated illumination opens up new regimes in control of proximity effects,1, 6, 7 and we will show corresponding examples of improved through-pitch performance in 22nm Resolution Enhancement Technique (RET). Simulation results will back-up the experimental results and detail the ability of SMO to drive exposure-count reduction, as well as a reduction in process variation due to critical factors such as Line Edge Roughness (LER), Mask Error Enhancement Factor (MEEF), and the Electromagnetic Field (EMF) effect. The benefits of running intensive optimization with both source and mask variables jointly has been previously discussed.1-3 This paper will build on these results by demonstrating large-scale jointly-optimized source/mask solutions and their impact on design-rule enumerated designs.

Conversational Bootstrapping and Other Tricks of a Concierge Robot

We describe the effective use of online learning to enhance the conversational capabilities of a concierge robot that we have been developing over the last two years. The robot was designed to interact naturally with visitors and uses a speech recognition system in conjunction with a natural language classifier. The online learning component monitors interactions and collects explicit and implicit user feedback from a conversation and feeds it back to the classifier in the form of new class instances and adjusted threshold values for triggering the classes. In addition, it enables a trusted master to teach it new question-answer pairs via question-answer paraphrasing, and solicits help with maintaining question-answer-class relationships when needed, obviating the need for explicit programming. The system has been completely implemented and demonstrated using the SoftBank Robotics [34] humanoid robots Pepper and NAO, and the telepresence robot known as Double from Double Robotics [4].

A multi-objective genetic algorithm using intermediate features of simulations

This paper proposes using intermediate features of traffic simulations in a genetic algorithm designed to find the best scenarios in regulating traffic with multiple objectives. A challenge in genetic algorithms for multi-objective optimization is how to find various optimal scenarios within a limited decision time. Typical evolutionary algorithms usually maintain a population of diversified scenarios whose diversity is measured only by the final objectives available at the end of their simulations. We propose measuring the diversity by also the time series of the objectives during the simulations. The intuition is that simulation scenarios with similar final objective values may contain different series of discrete events that, when combined, can result in better scenarios. We provide empirical evidence by experimenting with agent-based traffic simulations showing the superiority of the proposed genetic algorithm over standard approaches in approximating Pareto fronts.

Improving accessibility for existing websites spanning multiple domains

There are demands for improving accessibility in existing websites by enlarging text and changing the colors. Typical solutions use technologies such as ActiveX that can only run on a specific client environment. JavaScript is supported in many Web browsers, and it can be used to add new functions for improving the accessibility of existing websites. However, Web browsers prohibit JavaScript from accessing webpages of another domain, so it is difficult to improve accessibility for related websites spanning multiple domains. This paper describes a method that solves the problem.