Chasing carbon: The elusive environmental footprint of computing

U Gupta, YG Kim, S Lee, J Tse, HHS Lee… - … Symposium on High …, 2021‏ - ieeexplore.ieee.org
Given recent algorithm, software, and hardware innovation, computing has enabled a
plethora of new applications. As computing becomes increasingly ubiquitous, however, so …

A hierarchical framework of cloud resource allocation and power management using deep reinforcement learning

N Liu, Z Li, J Xu, Z Xu, S Lin, Q Qiu… - 2017 IEEE 37th …, 2017‏ - ieeexplore.ieee.org
Automatic decision-making approaches, such as reinforcement learning (RL), have been
applied to (partially) solve the resource allocation problem adaptively in the cloud computing …

Machine learning for power, energy, and thermal management on multicore processors: A survey

S Pagani, PDS Manoj, A Jantsch… - IEEE Transactions on …, 2018‏ - ieeexplore.ieee.org
Due to the high integration density and roadblock of voltage scaling, modern multicore
processors experience higher power densities than previous technology scaling nodes …

Energy-efficient datacenters

M Pedram - IEEE Transactions on Computer-Aided Design of …, 2012‏ - ieeexplore.ieee.org
Pervasive use of cloud computing and the resulting rise in the number of datacenters and
hosting centers (that provide platform or software services to clients who do not have the …

Reinforcement learning-assisted garbage collection to mitigate long-tail latency in SSD

W Kang, D Shin, S Yoo - ACM Transactions on Embedded Computing …, 2017‏ - dl.acm.org
NAND flash memory is widely used in various systems, ranging from real-time embedded
systems to enterprise server systems. Because the flash memory has erase-before-write …

Application and thermal-reliability-aware reinforcement learning based multi-core power management

SMP Dinakarrao, A Joseph, A Haridass… - ACM Journal on …, 2019‏ - dl.acm.org
Power management through dynamic voltage and frequency scaling (DVFS) is one of the
most widely adopted techniques. However, it impacts application reliability (due to soft …

Opportunities for machine learning in electronic design automation

PA Beerel, M Pedram - 2018 IEEE International Symposium on …, 2018‏ - ieeexplore.ieee.org
The rise of machine learning (ML) has introduced many opportunities for computer-aided-
design, VLSI design, and their intersection. Related to computer-aided design, we review …

Optimal DPM and DVFS for frame-based real-time systems

MET Gerards, J Kuper - ACM Transactions on Architecture and Code …, 2013‏ - dl.acm.org
Dynamic Power Management (DPM) and Dynamic Voltage and Frequency Scaling (DVFS)
are popular techniques for reducing energy consumption. Algorithms for optimal DVFS exist …

Model-free reinforcement learning and bayesian classification in system-level power management

Y Wang, M Pedram - IEEE Transactions on Computers, 2016‏ - ieeexplore.ieee.org
To cope with uncertainties and variations that emanate from hardware and/or application
characteristics, dynamic power management (DPM) frameworks must be able to learn about …

LifeGuard: A reinforcement learning-based task map** strategy for performance-centric aging management

V Rathore, V Chaturvedi, AK Singh… - Proceedings of the 56th …, 2019‏ - dl.acm.org
Device scaling to subdeca nanometer has pushed device aging as a primary design
concern. In manycore systems, inevitable process variation further adds to delay …