Publications by Marina Zapater Sancho

LIONHEART: A Layer-based Mapping Framework for Heterogeneous Systems with Analog In-Memory Computing Tiles

David Atienza Alonso, Giovanni Ansaloni, Irem Boybat Kara, Marina Zapater Sancho, Abu Sebastian, Flavio Ponzina, Joshua Alexander Harrison Klein

When arranged in a crossbar configuration, resistive memory devices can be used to execute Matrix-Vector Multiplications (MVMs), the most dominant operation of many Machine Learning (ML) algorithms, in constant time complexity. Nonetheless, when performing ...

Institute of Electrical and Electronics Engineers (IEEE)2025

SideDRAM: Integrating SoftSIMD Datapaths near DRAM Banks for Energy-Efficient Variable Precision Computation

David Atienza Alonso, Giovanni Ansaloni, Marina Zapater Sancho, Alexandre Sébastien Julien Levisse, Rafael Medina Morillas, Pengbo Yu

By interfacing computing logic directly to the DRAM banks, bank-level Compute-near-Memory (CnM) architectures promise to mitigate the bottleneck at the memory interconnect. While this computation paradigm heavily reduces the energy requirements for data mo ...

ACM2025

Towards Accurate RISC-V Full System Simulation via Component-level Calibration

David Atienza Alonso, Giovanni Ansaloni, Marina Zapater Sancho, Joshua Alexander Harrison Klein

Full-System (FS) simulation is essential for performance evaluation of complete systems that execute complex applications on a complete software stack consisting of an operating system and user applications. Nevertheless, they require careful fine-tuning a ...

Association for Computing Machinery (ACM)2025

Bank on Compute-near-Memory: Design Space Exploration of Processing-near-Bank Architectures

Giovanni Ansaloni, Marina Zapater Sancho, Rafael Medina Morillas

Near-DRAM computing strategies advocate for providing computational capabilities close to where data is stored. Although this paradigm can effectively address the memoryto-processor communication bottleneck, it also presents new challenges: The strict reso ...

Institute of Electrical and Electronics Engineers (IEEE)2024

CloudProphet: A Machine Learning-Based Performance Prediction for Public Clouds

David Atienza Alonso, Ali Pahlevan, Marina Zapater Sancho, Luis Maria Costero Valero, Darong Huang

Computing servers have played a key role in developing and processing emerging compute-intensive applications in recent years. Consolidating multiple virtual machines (VMs) inside one server to run various applications introduces severe competence for limi ...

2024

Intermediate Address Space: virtual memory optimization of heterogeneous architectures for cache-resident workloads

David Atienza Alonso, Marina Zapater Sancho, Luis Maria Costero Valero, Darong Huang, Qunyou Liu

The increasing demand for computing power and the emergence of heterogeneous computing architectures have driven the exploration of innovative techniques to address current limitations in both the compute and memory subsystems. One such solution is the use ...

2024

System-Level Exploration of In-Package Wireless Communication for Multi-Chiplet Platforms

David Atienza Alonso, Giovanni Ansaloni, Marina Zapater Sancho, Joshua Alexander Harrison Klein, Rafael Medina Morillas

Multi-Chiplet architectures are being increasingly adopted to support the design of very large systems in a single package, facilitating the integration of heterogeneous components and improving manufacturing yield. However, chiplet-based solutions have to ...

2023

REMOTE: Re-thinking Task Mapping on Wireless 2.5D Systems-on-Package for Hotspot Removal

David Atienza Alonso, Giovanni Ansaloni, Marina Zapater Sancho, Darong Huang, Rafael Medina Morillas

2.5D Systems-on-Package (SoPs) are composed by several chiplets placed on an interposer. They are becoming increasingly popular as they enable easy integration of electronic components in the same package and high fabrication yields. Nevertheless, they int ...

2023

Thermal and Power-Aware Run-Time Performance Management of 3D MPSoCs with Integrated Flow Cell Arrays

David Atienza Alonso, Halima Najibi, Giovanni Ansaloni, Marina Zapater Sancho, Alexandre Sébastien Julien Levisse

Flow Cell Arrays (FCA) technology employs microchannels filled with an electrolytic fluid to concurrently provide cooling and power generation to integrated circuits (ICs). This solution is particularly appealing for Three-Dimensional Multi-Processor Syste ...

2022

Reinforcement Learning-Based Joint Reliability and Performance Optimization for Hybrid-Cache Computing Servers

David Atienza Alonso, Ali Pahlevan, Marina Zapater Sancho, Luis Maria Costero Valero, Darong Huang

Computing servers play a key role in the development and process of emerging compute-intensive applications in recent years. However, they need to operate efficiently from an energy perspective viewpoint, while maximizing the performance and lifetime of th ...

2022

Thermal and Voltage-Aware Performance Management of 3D MPSoCs with Flow Cell Arrays and Integrated SC Converters

David Atienza Alonso, Halima Najibi, Giovanni Ansaloni, Marina Zapater Sancho, Alexandre Sébastien Julien Levisse

Flow cell arrays (FCAs) concurrently provide efficient on-chip liquid cooling and electrochemical power generation. This technology is especially promising for three-dimensional multi-processor systems-on-chip (3D MPSoCs) realized in deeply scaled technolo ...

2022

Full System Exploration of On-Chip Wireless Communication on Many-Core Architectures

David Atienza Alonso, Giovanni Ansaloni, Marina Zapater Sancho, Yasir Mahmood Qureshi, Joshua Alexander Harrison Klein, Rafael Medina Morillas

In order to develop sustainable and more powerful information technology (IT) infrastructures, the challenges posed by the "memory wall" are critical for the design of high-performance and high-efficiency many-core computing systems. In this context, recen ...

IEEE2022

COCKTAIL: Multi-Core Co-Optimization Framework With Proactive Reliability Management

David Atienza Alonso, Ali Pahlevan, Marina Zapater Sancho, Darong Huang

High performance computing (HPC) servers aim to meet an increase in the number and complexity of tasks and, consequently, to address the energy efficiency challenge. In addition to energy efficiency, it is essential to manage lifetime limitations of power- ...

2022

ALPINE: Analog In-Memory Acceleration with Tight Processor Integration for Deep Learning

David Atienza Alonso, Giovanni Ansaloni, Marina Zapater Sancho, Yasir Mahmood Qureshi, Alexandre Sébastien Julien Levisse, Joshua Alexander Harrison Klein

Analog in-memory computing (AIMC) cores offers significant performance and energy benefits for neural network inference with respect to digital logic (e.g., CPUs). AIMCs accelerate matrix-vector multiplications, which dominate these applications’ run-time. ...

2022

MAGNETIC: Multi-Agent Machine Learning-Based Approach for Energy Efficient Dynamic Consolidation in Data Centers

David Atienza Alonso, Ali Pahlevan, Marina Zapater Sancho, Kosar Haghshenas

Improving the energy efficiency of data centers while guaranteeing Quality of Service (QoS), together with detecting performance variability of servers caused by either hardware or software failures, are two of the major challenges for efficient resource m ...

2022

Gem5-X : A Many-Core Heterogeneous Simulation Platform for Architectural Exploration and Optimization

David Atienza Alonso, William Andrew Simon, Marina Zapater Sancho, Yasir Mahmood Qureshi

The increasing adoption of smart systems in our daily life has led to the development of new applications with varying performance and energy constraints, and suitable computing architectures need to be developed for these new applications. In this paper, ...

2021

ECOGreen: Electricity Cost Optimization for Green Datacenters in Emerging Power Markets

David Atienza Alonso, Ayse Kivilcim Coskun, Ali Pahlevan, Marina Zapater Sancho

Modern datacenters need to tackle efficiently the increasing demand for computing resources while minimizing energy usage and monetary costs. Power market operators have recently introduced emerging demand-response programs, in which electricity consumers ...

2021

Multi-Agent Reinforcement Learning for Hyperparameter Optimization of Deep Convolutional Neural Networks

David Atienza Alonso, Marina Zapater Sancho, Arman Iranfar

Nowadays, Deep Convolutional Neural Networks (DCNNs) play a significant role in many application domains, such as, computer vision, medical imaging, and image processing. Nonetheless, designing a DCNN, able to defeat the state of the art, is a manual, chal ...

2021

3D-ICE 3.0: efficient nonlinear MPSoC thermal simulation with pluggable heat sink models

David Atienza Alonso, Marina Zapater Sancho, Federico Terraneo

The increasing power density in modern high-performance multi-processor system-on-chip (MPSoC) is fueling a revolution in thermal management. On the one hand, thermal phenomena are becoming a critical concern, making accurate and efficient simulation a nec ...

2021

Associativity-agnostic in-cache computing memory architecture optimized for multiplication

David Atienza Alonso, Marina Zapater Sancho, Alexandre Sébastien Julien Levisse, Marco Antonio Rios

A random access memory array including a plurality of local memory group ways, each local memory group way including, a plurality of local memory groups, each local memory group including, a memory column including a plurality of memory cells, a pair of lo ...

2021