Publications de Arman Iranfar | EPFL Graph Search

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

ReLearn: A Robust Machine Learning Framework in Presence of Missing Data for Multimodal Stress Detection from Physiological Signals

David Atienza Alonso, Adriana Arza Valdes, Arman Iranfar

2021

Dynamic Thermal Management with Proactive Fan Speed Control Through Reinforcement Learning

David Atienza Alonso, Marina Zapater Sancho, Arman Iranfar, Federico Terraneo, Gabor Andras Csordas

Dynamic Thermal Management (DTM) has become a major challenge since it directly affects Multiprocessors Systems-on-chip (MPSoCs) performance, power consumption, and reliability. In this work, we propose a transient fan model, enabling adaptive fan speed co ...

IEEE2020

Containergy-A Container-Based Energy and Performance Profiling Tool for Next Generation Workloads

David Atienza Alonso, Marina Zapater Sancho, Arman Iranfar, Wellington Silva De Souza

Run-time profiling of software applications is key to energy efficiency. Even the most optimized hardware combined to an optimally designed software may become inefficient if operated poorly. Moreover, the diversification of modern computing platforms and ...

2020

Resource Management for Power-Constrained HEVC Transcoding Using Reinforcement Learning

David Atienza Alonso, Marina Zapater Sancho, Arman Iranfar, Luis Maria Costero Valero

The advent of online video streaming services along with the users' demand for high-quality contents require High Efficiency Video Coding (HEVC), which provides higher quality and compression at the cost of increased complexity. On one hand, HEVC exposes a ...

2020

Multi-Objective Management of Multiprocessor Systems: From Heuristics to Reinforcement Learning

Arman Iranfar

In my thesis, I reveal several already-existing and emerging challenges in multi-objective management of multiprocessor systems, and address them through novel solutions, from heuristics to RL, depending on the complexity of the problem. Conventional multi ...

EPFL2020

Enhancing Two-Phase Cooling Efficiency through Thermal- Aware Workload Mapping for Power-Hungry Servers

David Atienza Alonso, Marina Zapater Sancho, Arman Iranfar, Ali Pahlevan

The power density and, consequently, power hungriness of server processors is growing by the day. Traditional air cooling systems fail to cope with such high heat densities, whereas single-phase liquid-cooling still requires high mass flowrate, high pumpin ...

IEEE and ACM Press2019

MAMUT: Multi-Agent Reinforcement Learning for Efficient Real-Time Multi-User Video Transcoding

David Atienza Alonso, Marina Zapater Sancho, Arman Iranfar, Luis Maria Costero Valero

Real-time video transcoding has recently raised as a valid alternative to address the ever-increasing demand for video contents in servers' infrastructures in current multi-user environments. High Efficiency Video Coding (HEVC) makes efficient online trans ...

IEEE2019

A QoS and Container-Based Approach for Energy Saving and Performance Profiling in Multi-Core Servers

David Atienza Alonso, Marina Zapater Sancho, Arman Iranfar, Wellington Silva De Souza

In this work we present ContainEnergy, a new performance evaluation and profiling tool that uses software containers to perform application runtime assessment, providing energy and performance profiling data. It is focused on energy efficiency for next gen ...

IEEE2019

A Machine Learning-Based Framework for Throughput Estimation of Time-Varying Applications in Multi-Core Servers

David Atienza Alonso, Marina Zapater Sancho, Arman Iranfar, Wellington Silva De Souza

2019