Unlike traditional rasterization rendering, ray tracing is a groundbreaking technology that has revolutionized the realistic rendering of images, marking a significant leap forward. However, achieving real-time ray tracing in dynamic scene applications remains a challenging task. This difficulty arises primarily from the substantial technical bottlenecks related to the frequent need for reconstructing or incrementally updating acceleration structures essential for efficient ray calculations.

Ray tracing has emerged as a powerful technique for generating visually stunning and realistic images compared to rasterization. With the continuous advancements in computer hardware, modern GPUs have integrated specialized ray tracing acceleration units to enhance rendering capabilities further. However, achieving realtime ray tracing presents a challenge in dynamic scenes, where spatial data structures used for accelerated rendering must be reconstructed or updated when there are changes in the scene primitives. This paper introduces QuickTree, a novel Bounding Volume Hierarchy (BVH) construction engine based on the linear BVH (LBVH) optimization algorithm. QuickTree addresses the challenge of dynamic scenes support by employing a highly parallel and pipelined system design. This innovative approach ensures fast construction speed. QuickTree demonstrates significant performance improvements. Compared to the currently fastest MergeTree, it has increased construction speed by 10% and reduced area by 45% compared to RayCore, which has the smallest chip area.

Value prediction is a microarchitectural technique that enhances processor performance by speculatively breaking true data dependencies. It has demonstrated improved performance in both single-threaded and multi-threaded workloads, rendering it an appealing microarchitectural approach. While high-performance value predictors can achieve impressive accuracy, they may also incur significant costs in terms of area, power consumption, and complexity. Therefore, there is a demand for lightweight value prediction techniques capable of striking a favorable balance between performance and overhead. However, designing value predictors with superior performance using limited resources presents an urgent challenge. Consequently, this work proposes a design space exploration framework for the state-of-the-art EVES value predictor, aiming to efficiently configure the design parameters of the value predictor within constrained RAM resources. Additionally, the article evaluates the performance of the explored value predictor across a wide range of workloads. The explored value predictors exhibit high efficiency across RAM sizes ranging from 2KB to 16KB while maintaining acceptable computational complexity. Furthermore, the results indicate that the explored value predictor achieves optimal efficiency under the 2KB constraint, with the highest acceleration-to-cost ratio reaching 4.02%/KB.

Ray tracing has been regarded as the future of graphics rendering technology for a long time. However, interactive ray tracing still faces challenges, especially in mobile devices, such as high computational intensity and multiple branches. In this brief, we aim to maximize overall efficiency by leveraging all forms of potential parallelism, including task, basic block, loop, and pipeline levels. We present multilevel parallel ray tracing accelerator (MPRTA), an innovative mobile accelerator that offers high performance and optimal efficiency for ray tracing. Experimental results indicate that MPRTA is$1.67\times $more efficient than the currently best-reported mobile accelerator.