This paper proposes a high-throughput lossless image-compression algorithm based on Golomb–Rice coding and its hardware architecture. The proposed solution increases compression ratios (CRs) while preserving the throughput by taking advantage of a novel parallel variable-length sign coding (PVSC) algorithm that reduces the sign bits to achieve a higher CR. In addition, the proposed solution adopts and modifies the two existing compression algorithms to improve the overall compression performance. The experimental results show that the proposed solution yields an average CR of 3.12, which is higher than those achieved with the previous algorithms. The hardware implementation of the proposed solution for an 8 × 8 block unit achieves a throughput of 18 GBps and 24 GBps when encoding and decoding, respectively. This hardware performance is enough to handle 7680×4320@240-Hz image processing.

With the fast-growing development of mobile devices, the application of high-end three-dimensional (3-D) graphics is expanding to include usage in mobile platforms. Recent mobile application processors are equipped with a multicore CPU and a mobile GPU on a single chip, thereby enabling the incorporation of high-end 3-D graphics into mobile devices. The problem is that such features consume large amounts of power. Thus, previous studies focused on estimating the power consumption of mobile GPUs, but such research has been unfamiliar with user perspective. To address this deficiency, the current work developed a novel performance prediction model for mobile GPUs on Adreno. The model uses both the instruction throughput of a unified shader and GFLOPS. The utilization of the Adreno GPUs was adjusted to its maximum value to ensure that their performance is unaffected by dynamic voltage and frequency scaling and throttling functions. The model was validated using GFXBench under real game application environments. The simulation results provided the computational rates of each hardware unit of the Adreno GPUs and the rate of increase in the instruction processing of the unified shader. To verify the accuracy of the model, we compared the difference rates of the prediction results between those derived from the proposed model and those using Snapdragon profiler. The average error rate was 3.32% with three applications running on four different mobile devices.

In this paper, we propose an effective algorithm of pipelined dividers for double-precision floating-point numbers. This reduces the latency of the previous pipelined dividers without increasing the lookup table size. The experimental results show that the proposed divider reduces the overall latency up to 16% compared to the previous divider.

The power consumption models of mobile application processors have emerged as key objects of interest following the tremendous growth in mobile device production given that such consumption is an important factor in the graphics performance of mobile technologies. Conventionally, the performance of the graphics processing units (GPUs) depends critically on texture mapping units, which is why the number of such GPU components and texture fill rates value prominently whenever the GPU performance is evaluated. Our previous work has established a model to predict maximum performance based on unified shaders. By extending the work, this paper developed a practically applicable GPU performance prediction model on the basis of texture mapping performance. The effects of increased texture mapping units on unified shader performance and GPU efficiency were examined, and a performance prediction model based on the number of frames per second (FPS) was constructed. For these purposes, a benchmark related to texture mapping units was formulated and the experiments were conducted to determine utilization factors that are relevant to GPU performance and efficiency. The final stage in model construction involved establishing a relationship between the previously investigated utilization factors and relevant resources that are consumed during graphics processing. The experimental results showed that the proposed prediction model produced an average error rate of 5.77%.