ARCO - Microarquitectura i Compiladors
Les activitats de recerca del grup ARCO es centren en l'area de arquitectura de computadors, compiladors i el processament en paral·lel, amb especial èmfasi en la microarquitectura i les tècniques de generació de codi per a sistemes de computació fiables i eficients energèticament. Un dels seus principals enfocaments actuals és sobre sistemes de computació intel·ligents, on l'objectiu és dissenyar noves arquitectures per a l'aprenentatge automàtic, la visió per computador i el processament del llenguatge. L'altre enfocament principal és en els processadors gràfics tant per a càrregues de treball de propòsit general com per a aplicacions gràfiques.
El grup està format per professors i estudiants de la Universitat Politècnica de Catalunya, la Universitat de Múrcia i la Universitat Rovira i Virgili. El grup té un llarg historial de publicacions científiques, amb més de 500 articles d'investigació, i transferències de tecnologia, amb més de 50 patents.
Las actividades de investigación del grupo ARCO se centran en el área de arquitectura de computadores, compiladores y el procesamiento en paralelo, con especial énfasis en la microarquitectura y las técnicas de generación de código para sistemas de computación fiables y eficientes energéticamente. Uno de sus principales enfoques actuales es sobre sistemas de computación inteligentes, donde el objetivo es diseñar nuevas arquitecturas para el aprendizaje automático, la visión por computador y el procesamiento del lenguaje. El otro enfoque principal es en los procesadores gráficos tanto para cargas de trabajo de propósito general como para aplicaciones gráficas.
El grupo está formado por profesores y estudiantes de la Universidad Politécnica de Catalunya, la Universidad de Murcia y la Universidad Rovira i Virgili. El grupo tiene un largo historial de publicaciones científicas, con más de 500 artículos de investigación, y transferencias de tecnología, con más de 50 patentes.
The research activities of the ARCO group focus on computer architecture, compilers and parallel processing, with special emphasis on microarchitecture and code generation techniques for energy-efficient and reliable computing systems. One of its main current focuses is on intelligent computing systems, where the goal is to devise novel architectures for machine learning, computer vision, language processing. The other major focus is on graphics processors both for general-purpose and graphics workloads.
The group consists of faculty members and students from Polytechnic University of Catalonia, University of Murcia and Rovira i Virgili University. The group has a long track record of scientific publications, with more than 500 research papers, and technology transfers, with more than 50 patents.
The research activities of the ARCO group focus on computer architecture, compilers and parallel processing, with special emphasis on microarchitecture and code generation techniques for energy-efficient and reliable computing systems. One of its main current focuses is on intelligent computing systems, where the goal is to devise novel architectures for machine learning, computer vision, language processing. The other major focus is on graphics processors both for general-purpose and graphics workloads.
The group consists of faculty members and students from Polytechnic University of Catalonia, University of Murcia and Rovira i Virgili University. The group has a long track record of scientific publications, with more than 500 research papers, and technology transfers, with more than 50 patents.
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Articles de revista [73]
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Reports de recerca [14]
Recent Submissions
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Parallelizing a modern GPU simulator
(2024)
Research report
Open AccessSimulators are a primary tool in computer architecture research but are extremely computationally intensive. Simulating modern architectures with increased core counts and recent workloads can be challenging, even on modern ... -
Hamun: an approximate computing method to prolong the lifespan of ReRAM-based accelerators
(Elsevier, 2025-09)
Article
Open AccessReRAM-based accelerators exhibit enormous potential to increase computational efficiency for DNN inference tasks, delivering significant performance and energy savings over traditional platforms. By incorporating adaptive ... -
LIBRA: memory bandwidth- and locality-aware parallel tile rendering
(Institute of Electrical and Electronics Engineers (IEEE), 2024)
Conference report
Open AccessThe increasing demand for high-quality graphics requires a significant increase in computational power of modern GPUs. The common approach to follow is augmenting the number of compute units (i.e., shader cores). However, ... -
SIMIL: SIMple Issue Logic for GPUs
(2024-11)
Article
Open AccessGPU architectures have become popular for executing general-purpose programs. In particular, they are some of the most efficient architectures for machine learning applications which are among the most trendy and demanding ... -
ReDy: a novel ReRAM-centric dynamic quantization approach for energy-efficient CNNs
(Association for Computing Machinery (ACM), 2024)
Conference report
Open AccessDeep Neural Networks (DNNs) have achieved enormous success for a large variety of applications. The primary operation in DNNs is the dot product of quantized input activations and weights. Prior works have proposed the ... -
An energy-efficient near-data processing accelerator for DNNs to optimize memory accesses
(Elsevier, 2025-02)
Article
Open AccessThe constant growth of DNNs makes them challenging to implement and run efficiently on traditional computecentric architectures. Some accelerators have attempted to add more compute units and on-chip buffers to solve the ... -
Mixture-of-Rookies: saving DNN computations by predicting ReLU outputs
(Elsevier, 2024-09)
Article
Open AccessDeep Neural Networks (DNNs) are widely used in many application domains. However, they require a vast amount of computations and memory accesses to deliver outstanding accuracy. In this paper, we propose a scheme to predict ... -
Exploiting beam search confidence for energy-efficient speech recognition
(Springer, 2024-07-15)
Article
Open AccessWith mobile and embedded devices getting more integrated in our daily lives, the focus is increasingly shifting toward human-friendly interfaces, making automatic speech recognition (ASR) a central player as the ideal means ... -
Memento: an adaptive, compiler-assisted register file cache for GPUs
(Institute of Electrical and Electronics Engineers (IEEE), 2024)
Conference report
Open AccessModern GPUs require an enormous register file (RF) to store the context of thousands of active threads. It consumes considerable energy and contains multiple large banks to provide enough throughput. Thus, a RF caching ... -
SLIDEX: A novel architecture for sliding window processing
(Association for Computing Machinery (ACM), 2024)
Conference report
Open AccessEfficient image processing is increasingly crucial in constrained embedded and real-time platforms, especially in emerging applications such as Autonomous Driving (AD) or Augmented/Virtual Reality (AR/VR). A commonality ... -
DNA-TEQ: an adaptive exponential quantization of tensors for DNN inference
(Institute of Electrical and Electronics Engineers (IEEE), 2023)
Conference report
Open AccessQuantization is commonly used in Deep Neural Networks (DNNs) to reduce the storage and computational complexity by decreasing the arithmetical precision of activations and weights, a.k.a. tensors. Efficient hardware ... -
Boosting point cloud search with a vector unit
(2023)
Research report
Open AccessModern robots collect and process point clouds to perform accurate registration and segmentation. The most time-consuming kernel within point cloud processing -namely neighbor search- relies on appropriate data structures, ...
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