Ray tracing (RT) is a rendering technique that produces high-fidelity images by simulating how light physically interacts with objects in a scene.This realism comes at a high computational and memory cost, largely driven by the need to find numerous ray-object intersections.To accelerate this process, scenes are typically structured using bounding volume hierarchy (BVH) trees, where objects are grouped within bounding boxes to minimize the number of required intersection tests.Specialized hardware, known as RT accelerators, accelerates BVH processing to boost computational speed, yet memory traffic persists as a major bottleneck, largely due to the bandwidth consumed by standard 32-bit floating-point (FP32) bounding boxes.Consequently, previous work has aimed to reduce memory traffic by compressing these boxes into low-bit (e.g., 8-bit) representations.However, existing compression techniques typically require decompressing bounding boxes back to FP32 for intersection tests, thus failing to eliminate the computational burden and energy cost associated with complex FP32 arithmetic during BVH traversal.This work introduces AQB8, an RT accelerator designed to operate on a quantized BVH tree constructed using a novel multi-level quantization technique.This approach enables RT to operate directly on low-bit integers using simpler, area-efficient hardware units, thereby drastically reducing the need for FP32 arithmetic during BVH traversal while mitigating overheads associated with reduced precision.As a result, across various scenes, AQB8 achieves a 70% reduction in DRAM accesses, a 49% reduction in energy consumption, a 27% hardware area reduction, and a 1.82x performance speedup over modern GPU RT accelerators.Our code is publicly available at https://github.com/nycu-caslab/AQB8.

Model Driven Architecture (MDA) is a new software development framework. This paper presents a model-driven approach to the development of Web applications by combining Conallen's web applications design concept and Kleppe's MDA process. We use the UML extension mechanism, i.e. stereotypes, to define the various web elements, and use the Robustness diagram to represent MVC 2 structure for Web application. After required analysis, we start by using a use case diagram as CIM, and then transform CIM to PIM, and PIM to PSM. We propose mapping rules for model-to-model transformation. Finally, we develop a tool named WebPSM2Code, which can automatically transform PSM diagram to Web application code, such as Java, JSP, HTML, Servlet, Javascript, as well as deployment descriptor file. All the files can automatically address to the correct directory structure for JSP Web application, and the transformation rate is about 39% of the whole system. Using this methodology, systems can be analyzed, designed, and generated more easily and systematically. Thereby, the time that Web programmers spend on coding can be reduced.

To model time-dependent user intent for Web search, this paper proposes a novel method using machine learning techniques to exploit temporal features for effective time-sensitive search result re-ranking. We propose models to incorporate users' click through information for queries that are seen in the training data, and then further extend the model to deal with unseen queries considering the relationship between queries. Experiment shows significant improvement on search result ranking over original search outputs.

A novel memory repair architecture based on redundant hypercube is proposed, which mainly consists of a modified ternary CAM with an address bubble-shifter. Generally for an acceptable repair rate about 3% of spare sub cubes and no more than 5% of hardware overhead are required. A modified Quine-McCluskey algorithm and the Essential Cube Pivoting algorithm are also developed for redundancy analysis. Almost 100% of repair rate can be obtained using only 32 equivalent rows under reasonable situations. Under less spare memory the repair rates of proposed approaches can be much higher than most results of previous work.