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An Interview with Antonio González, Professor of Computer Architecture at Universitat Politècnica de Catalunya (UPC),  2025 B. Ramakrishna Rau Award Recipient. His groundbreaking work in processor microarchitecture, compiler optimization, and energy-aware computing has shaped the evolution of modern computing systems. As the founder of the ARCO research group and former director of the Intel Barcelona Research Center, Dr. González has led transformative research across decades—from speculative execution and clustered architectures to GPU efficiency and cognitive computing. We connected with Dr. González to explore his enduring impact on computer architecture and his vision for sustainable, high-performance systems.You founded and have led the ARCO research group at UPC since 1989. What have been the most impactful contributions of ARCO in advancing processor microarchitecture and compiler optimization over the past three decades?Over the past 35 years, our research has evolved in response to the challenges we anticipated for future computer systems. In our early work, we focused on data value and data dependence speculation, developing innovative techniques to predict and break data dependences among instructions. During this initial period, we also put a special focus on improving the architecture of the memory hierarchy. We were among the first to propose cache architectures with specialized modules for different types of locality, register file caches, and register file architectures with heterogeneous banks. Later, our attention shifted to multi-core processors. We developed disruptive techniques to exploit parallelism in multicore processors through the dynamic generation of speculative threads.By the late 1990's, we anticipated that energy consumption would become the primary bottleneck for future microprocessors, Consequently, we developed a wide range of energy-efficient microarchitectures, such as an adaptive instruction issue queues to reduce its energy consumption. Another major line of our research was clustered microarchitectures, which offer important advantages in both performance—by reducing wire delays—and energy efficiency—by simplifying…

The benefits of STEAM methodology for students abound, from advanced critical thinking and problem-solving skills to the application of abstract mathematical concepts and even frustration management. But STEAM learning can be difficult because it is hard to build STEM hands-on learning opportunities within the confines of a traditional classroom.To introduce a new way to incorporate STEAM for teachers and students, we held the IEEE CS Junior Camp at the Espacio Ciencia facilities within LATU (Uruguayan Technological Laboratory) in Montevideo, Uruguay, from 2 to 3 October 2025. The sessions exposed participants to drone programming for kids, Micro:bit student projects, and educational technology workshops. The result was a win for inclusive STEAM education programs in Latin America—and a prototype for STEAM learning worldwide.Solving Problems with Programming and Various TechnologiesThe program presented students with three challenges using a STEAM framework. To solve each challenge, students had to manipulate drones and programmable boards using Micro:bits. Many had little to no prior experience with Micro:bits or robotics, so the activities exposed them to a range of career possibilities in STEAM fields. The reactions were very positive: All students said they would recommend participation to a friend. 75% of student respondents preferred the drone programming, saying it was engaging, motivating, and enjoyable, while the remaining 25% also liked the piano programming activity. Survey responses also indicated that students enjoyed learning things they had never been exposed to before, exploring new technologies, and working with students from other institutions. Inclusive, Balanced, Mixed STEAM LearningThe event united 69 students and 14 teachers hailing from six institutions, both public and private.Instead of having students work with their immediate peers, the program used inter-institutional groups consisting of learners from different schools. Overall, this approach was a hit, with 10 out of 12 responding students saying they liked working with those…

A step by step guide on how to use LLM + GNN duo to get structure in Data Lakes.Data Lake is a centralized storage repository that is used to store raw data from multiple sources in a structured, semi-structured and unstructured format.Imagine a data lake that is full of CSVs, JSON files, logs, spreadsheets that nobody even remembers or knows how they relate. How would you even begin to query across it to get meaningful and useful data? This is where schema discovery comes into the rescue.A schema is the description or in other terms the blueprint of how data has been structured and organized. Schema discovery is the process of finding or identifying schemas and registering them.Large Language Model(LLM) is an AI model that can understand and process human language and generate human-like text. Graph Neural Network(GNN) is a special type of neural network that is designed to work with data organized in the form of a graph.This article is going to talk about the LLM + GNN duo as a solution and show how to move from chaos to structure. We’ll start by looking at the problems with a chaotic data lake and the characteristics of a chaotic data lake. Then we look into why schema discovery is needed and finally we go into depth and discuss how to use LLMs and GNNs to get meaningful structures from chaotic data lakes.Chaos in Data LakesA chaotic data lake is one that is filled with disorganized, low-quality and unmanaged data. Chaotic data lakes make it difficult for Data Engineers and Scientists to access, analyze and use it effectively.A chaotic data lake is characterized by the following; Low quality data - Such data is usually inaccurate, inconsistent or has incomplete information. The cause of this is lack of data validation measures…