The collaboration between trailblazing engineers and industry professionals has led to a groundbreaking technique called Skia, which may transform the future of computing efficiency for modern data centers.

In data centers, large computers process massive amounts of data, but often struggle to keep up due to taxing workloads. This results in slower performance, causing search engines to generate answers more slowly or not at all. To address this issue, researchers at Texas A&M University have developed Skia in collaboration with Intel, AheadComputing, and Princeton.

The team includes Dr. Paul V. Gratz, a professor in the Department of Electrical and Computer Engineering, Dr. Daniel A. Jiménez, a professor in the Department of Computer Science and Engineering, and Chrysanthos Pepi, a graduate student in the Department of Electrical and Computer Engineering.

Processing instructions has become a major bottleneck in modern processor design,” Gratz said. “We developed Skia to better predict what’s coming next and alleviate that bottleneck.” Skia can not only help better predict future instructions but also improve the throughput of instructions on the system, leading to quicker performance and less power consumption for the data center.

Think of throughput in terms of being a server in a restaurant,” Gratz said. “You have lots and lots of jobs to do. How many tasks can you complete or how many instructions can you execute per unit time? You want high throughput, especially for computing.”

Improving throughput can lead to quicker performance and less power consumption for the data center. In fact, making it up to 10% more efficient means a company previously needing to make 100 data centers around the country now only needs to make 90, which is 10 fewer data centers. That’s pretty significant. These data centers cost millions of dollars, and they consume roughly the equivalent of the entire output of a power plant.

Skia identifies and decodes these shadow branches in unused bytes, storing them in a memory area called the Shadow Branch Buffer, which can be accessed alongside the BTB. What makes this technique interesting is that most of the future instructions were already available, and we demonstrate that Skia, with a minimal hardware budget, can make data centers more efficient, nearly twice the performance improvement versus adding the same amount of storage to the existing hardware as we observe,” Pepi said.

Their findings, “Skia: Exposing Shadow Branches,” were published in one of the leading computer architecture conferences, the ACM International Conference on Architectural Support for Programming Languages and Operating Systems. The team also traveled to the Netherlands to present their work to colleagues from around the globe.

Funding for this research is administered by the Texas A&M Engineering Experiment Station (TEES), the official research agency for Texas A&M Engineering.

The COVID-19 pandemic has brought to the forefront the critical importance of thorough disinfection, particularly within hospital environments. However, traditional manual disinfection methods have inherent limitations, including labor shortages due to physical fatigue and risk of exposure to pathogens, inconsistent human performance, and difficulty in reaching obscured or hard-to-reach areas.

To address these challenges, a team of researchers from Pohang University of Science and Technology (POSTECH) has developed an “Intelligent Autonomous Wiping and UV-C Disinfection Robot” that can automate hospital disinfection processes. This innovative robot is capable of navigating through hospital environments and performing disinfection tasks with precision and consistency.

The key feature of this robot is its dual disinfection system, which combines physical wiping and UV-C irradiation to effectively remove contaminants from surfaces. The robotic manipulator uses a wiping mechanism to physically clean high-touch areas, while the UV-C light ensures thorough disinfection of hard-to-reach corners and narrow spaces.

Real-world testing at Pohang St. Mary’s Hospital validated the robot’s performance, with bacterial culture experiments confirming its effectiveness in disinfecting surfaces. Repeated autonomous operations were carried out to verify its long-term usability in clinical settings.

The significance of this technology lies in its ability to automate time-consuming and repetitive disinfection tasks, allowing healthcare professionals to devote more attention to patient care. Additionally, the robot’s precision control algorithms minimize operational failures, while its integration with a self-sanitizing station and wireless charging system ensures sustained disinfection operations.

Professor Keehoon Kim emphasized that despite COVID-19 transitioning into an endemic phase, it remains essential to prepare for future pandemics by advancing this disinfection robot technology beyond hospitals to public facilities, social infrastructures, and everyday environments to further reduce infection risks. This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT).

The hospitality industry is taking a cue from new research in the Penn State School of Hospitality Management, which suggests that service robots can be designed to influence customers’ decisions based on their gender characteristics. The study found that service robots with characteristics typically associated with males may be more persuasive when interacting with women who have a low sense of power.

Led by researchers Lavi Peng, Anna Mattila, and Amit Sharma, the team conducted two studies to explore how the gender portrayed in service robots can affect customers’ decisions. In the first study, participants were asked to imagine visiting a new restaurant and receiving a menu recommendation from a service robot. The results showed that women with a low sense of power were more likely to accept recommendations from male robots.

“For men with a low sense of power, we found the difference was less obvious,” said Peng. “Based on our findings, consumers with high power tend to make their own judgment without relying on societal expectations.”

The researchers suggested that businesses could leverage these findings by using male robots to recommend new menu items or persuade customers to upgrade their rooms.

To mitigate gender stereotypes in robot design, the team conducted a second study and found that “cute” features, such as big eyes and raised cheeks, can reduce the effect of portrayed robot gender on persuasiveness. Both male and female customers responded similarly to robots with these features, suggesting that businesses could consider using cute designs to mitigate gender stereotypes.

The Marriott Foundation supported this research, highlighting the importance of understanding how service robots can influence customer decisions in the hospitality industry.