L4L: Experience-driven computational resource control in federated learning

Y Zhan, P Li, L Wu, S Guo - IEEE Transactions on Computers, 2021 - ieeexplore.ieee.org
As the large-scale deployment of machine learning applications, there is much research
attention on exploiting a vast amount of data stored on mobile clients. To preserve data …

Declarative power sequencing

J Schult, D Schwyn, M Giardino, D Cock… - ACM Transactions on …, 2021 - dl.acm.org
Modern computer server systems are increasingly managed at a low level by baseboard
management controllers (BMCs). BMCs are processors with access to the most critical parts …

[HTML][HTML] Low-Overhead Reinforcement Learning-Based Power Management Using 2QoSM

M Giardino, D Schwyn, B Ferri, A Ferri - Journal of Low Power Electronics …, 2022 - mdpi.com
With the computational systems of even embedded devices becoming ever more powerful,
there is a need for more effective and pro-active methods of dynamic power management …

Software framework of control systems on an MPSoCs platform

PH Vancin - 2023 - tede2.pucrs.br
With the increasing complexity of robotic systems, many aspects of their control system
architecture also become more complex. Sensing produces huge data aggregates to collect …

2QoSM: A Q-Learner QoS Manager for Application-Guided Power-Aware Systems

MJ Giardino, D Schwyn, B Ferri… - 2021 IEEE 14th …, 2021 - ieeexplore.ieee.org
This paper describes the design and performance of Q-learning-based quality-of-service
manager (2QoSM) for compute-aware applications (CAAs) as part of platform-agnostic …