University of Cantabria

The Computer Engineering Group studies architectural and technological innovations for next-generation computing systems beyond Moore’s Law. Our research spans processor microarchitecture, chip multiprocessors, memory hierarchies, interconnection networks, coherence protocols, and cache architectures, with growing emphasis on optimizing modern workloads such as large language models. We also investigate heterogeneous systems and specialized accelerators to deliver higher performance and efficiency under power and complexity constraints, from mobile platforms to supercomputers.

Selected Publications

More Publications

Publications by Topic

Rotary Router: an Efficient Architecture for CMP Interconnection Networks
Pablo Abad, Valentin Puente, Jose Angel Gregorio, Pablo Prieto , International Symposium on Computer Architecture, ISCA

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Flask Coherence: A Morphable Hybrid Coherence Protocol to Balance Energy, Performance and Scalability
Lucia G. Menezo, Valentin Puente, Jose Angel Gregorio , In International Symposium On High Performance Computer Architecture, IEEE., HPCA

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Architecture of a Cortex Inspired Hierarchical Event Recaller
Valentin Puente , ArXiv, ArXiv

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AC-WAR: Architecting the Cache Hierarchy to Improve the Lifetime of an Non-volatile Endurance-limited Main Memory
Pablo Abad, Pablo Prieto, Valentin Puente, Jose Angel Gregorio , Transactions on Parallel and Distributed Systems, IEEE., TPDS

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3D Stacking of High-Performance Processors
Philip Emma, Alper Buyuktosunoglu, Michael Healy, Krishnan Kailas, Valentin Puente, Roy Yu, Allan Hartstein, Pradip Bose, Jaime Moreno , In International Symposium On High Performance Computer Architecture, IEEE., HPCA

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ESP-NUCA: A low-cost adaptive Non-Uniform Cache Architectures
Javier Merino, Valentin Puente, Jose Angel Gregorio , International Conference on High Performance Computer Architecture, HPCA

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Projects

Closed Projects

Evaluation of 'Edge Computing' architectures for the inference of large language models
This project evaluates how small language models perform on edge devices by comparing inference across CPU, GPU, and NPU hardware. It focuses on trade-offs in latency, throughput, memory usage, and energy efficiency under realistic deployment constraints. The goal is to identify optimal backend choices and key bottlenecks to guide efficient on-device LLM deployment.
Rethinking CPU Backend for emerging general-purpose applications
This project proposes redesigning the backend of general-purpose processors to execute AI workloads more efficiently, supporting Europe's goals of AI competitiveness and digital sovereignty. Based on profiling of 31 deep learning models, it identifies vector execution resources in current CPUs as the main performance bottleneck. The central hypothesis is that a modular, slice-based backend architecture can scale SIMD throughput more effectively than conventional monolithic designs while preserving CPU flexibility and broad applicability.

Resources

Computing Resources
The local available computing resources and their current operational status
Tools
Computer architecture modeling tools developed by our group

Teaching

Digital Systems
Computer Systems Engineering (1st course)
Computer Architecture and Engineering
Computer Systems Engineering (3rd course)
Computer Systems (Administration)
Computer Systems Engineering (3rd course)
Introduction to Programming
No me se como se llama el titulo (1st course)
Operating Systems
Telecomunications Systems Engineering
Advanced Operating Systems
Computer Science Degree (4th course)
Computer Systems Design and Administration
Computer Systems Engineering (4th course)
Systems Security and Virtualization
Master in Computer Science (1st year)

University of Cantabria

Members

Faculty

PhD Students

Undergraduate Students

Selected Publications

Projects

Resources

Teaching

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