| Christian Hakert, Mikail Yayla, Kuan-Hsun Chen, Georg von der Brüggen, Jian-Jia Chen, Sebastian Buschjäger, Katharina Morik, Paul R. Genssler, Lars Bauer, Hussam Amrouch and Jörg Henkel.
Stack Usage Analysis for Efficient Wear Leveling in Non-Volatile Main Memory Systems.
In 1st ACM/IEEE Workshop on Machine Learning for CAD (MLCAD)
Alberta, Canada, 2019
[BibTeX][Abstract]@inproceedings { mlcad2019stackanalysis,
author = {Hakert, Christian and Yayla, Mikail and Chen, Kuan-Hsun and Br\"uggen, Georg von der and Chen, Jian-Jia and Buschj\"ager, Sebastian and Morik, Katharina and Genssler, Paul R. and Bauer, Lars and Amrouch, Hussam and Henkel, J\"org},
title = {Stack Usage Analysis for Efficient Wear Leveling in Non-Volatile Main Memory Systems},
booktitle = {1st ACM/IEEE Workshop on Machine Learning for CAD (MLCAD) },
year = {2019},
address = {Alberta, Canada},
keywords = {kuan, nvm-oma, georg},
confidential = {n},
abstract = {Emerging non-volatile memory (NVM) technologies, such as Phase Change Memory (PCM), have been considered as a replacement for DRAM and storage due to their low power consumption, fast access speed, and low unit cost. Even so, some NVMs have a significantly lower write endurance and hence in-memory wear leveling is an important requirement for practical applicability. Since writes to the stack often target a small and dense memory region, generic, coarse-grained wear-leveling mechanisms (e.g. virtual memory page remapping) are not sufficient. An alternative solution is to relocate the stack memory regularly, which involves copying of the stack content. As the stack content changes in size during the execution of an application, the copy overhead can be significantly mitigated by performing the relocation when the stack size is small. In this paper, we investigate two approaches to determine points in time when the stack is small. First, we analyze the possibility to fit simple machine-learning models to the stack usage function. Precise predictions of this function enable the identification of the minimum stack size during execution. In our evaluation, the tested models provide accurate estimates of the future stack usage function for a subset of common applications. As a second approach, we analyze applications a priori and determine potential optimal points to perform relocation in the instruction stream. In detail, we deploy the application in an analysis environment, which determines a rating for each executed instruction. Based on this rating, we apply a genetic algorithm to identify the best points in the instruction stream to perform the stack relocation. This approach allows to save up to 85% of the write overhead for wear-leveling in our experiments.},
}
Emerging non-volatile memory (NVM) technologies, such as Phase Change Memory (PCM), have been considered as a replacement for DRAM and storage due to their low power consumption, fast access speed, and low unit cost. Even so, some NVMs have a significantly lower write endurance and hence in-memory wear leveling is an important requirement for practical applicability. Since writes to the stack often target a small and dense memory region, generic, coarse-grained wear-leveling mechanisms (e.g. virtual memory page remapping) are not sufficient. An alternative solution is to relocate the stack memory regularly, which involves copying of the stack content. As the stack content changes in size during the execution of an application, the copy overhead can be significantly mitigated by performing the relocation when the stack size is small. In this paper, we investigate two approaches to determine points in time when the stack is small. First, we analyze the possibility to fit simple machine-learning models to the stack usage function. Precise predictions of this function enable the identification of the minimum stack size during execution. In our evaluation, the tested models provide accurate estimates of the future stack usage function for a subset of common applications. As a second approach, we analyze applications a priori and determine potential optimal points to perform relocation in the instruction stream. In detail, we deploy the application in an analysis environment, which determines a rating for each executed instruction. Based on this rating, we apply a genetic algorithm to identify the best points in the instruction stream to perform the stack relocation. This approach allows to save up to 85% of the write overhead for wear-leveling in our experiments.
|
| Mikail Yayla, Anas Toma, Kuan-Hsun Chen, Lenssen, Victoria Shpacovitch, Roland Hergenröder, Frank Weichert and Jian-Jia Chen.
Nanoparticle Classification Using Frequency Domain Analysis on Resource-Limited Platforms.
Journal of Sensors 19 4318
October 2019
[BibTeX][PDF][Link]@article { sensors-2019,
author = {Yayla, Mikail and Toma, Anas and Chen, Kuan-Hsun and Lenssen, and Shpacovitch, Victoria and Hergenr\"oder, Roland and Weichert, Frank and Chen, Jian-Jia},
date-added = {2019-11-15 21:41:55 +0000},
date-modified = {2019-11-15 21:45:29 +0000},
title = {Nanoparticle Classification Using Frequency Domain Analysis on Resource-Limited Platforms},
journal = {Journal of Sensors},
year = {2019},
volume = {19},
number = {4318},
month = {October},
url = {https://www.mdpi.com/1424-8220/19/19/4138},
keywords = {kuan},
file = {https://www.mdpi.com/1424-8220/19/19/4138/pdf},
confidential = {n},
} |
| Mikail Yayla, Anas Toma, Jan Eric Lenssen, Victoria Shpacovitch, Kuan-Hsun Chen, Frank Weichert and Jian-Jia Chen.
Resource-Efficient Nanoparticle Classification Using Frequency Domain Analysis.
In BVM Workshop
Lübeck, Germany, March 2019
[BibTeX][Abstract]@inproceedings { Yayla-BVM2019,
author = {Yayla, Mikail and Toma, Anas and Lenssen, Jan Eric and Shpacovitch, Victoria and Chen, Kuan-Hsun and Weichert, Frank and Chen, Jian-Jia},
title = {Resource-Efficient Nanoparticle Classification Using Frequency Domain Analysis},
booktitle = {BVM Workshop},
year = {2019},
address = {L\"ubeck, Germany},
month = {March},
keywords = {kuan},
confidential = {n},
abstract = {We present a method for resource-efficient classification of nanoparticles such as viruses in liquid or gas samples by analyzing Surface Plasmon Resonance (SPR) images using frequency domain features. The SPR images are obtained with the Plasmon Assisted Microscopy Of Nano-sized Objects (PAMONO) biosensor, which was developed as a mobile virus and particle detector. Convolutional neural network (CNN) solutions are available for the given task, but since the mobility of the sensor is an important factor, we provide a faster and less resource demanding alternative approach for the use in a small virus detection device.The execution time of our approach, which can be optimized further using low power hardware such as a digital signal processor (DSP), is at least 2.6 times faster than the current CNN solution while sacrificing only 1 to 2.5 percent points in accuracy.},
}
We present a method for resource-efficient classification of nanoparticles such as viruses in liquid or gas samples by analyzing Surface Plasmon Resonance (SPR) images using frequency domain features. The SPR images are obtained with the Plasmon Assisted Microscopy Of Nano-sized Objects (PAMONO) biosensor, which was developed as a mobile virus and particle detector. Convolutional neural network (CNN) solutions are available for the given task, but since the mobility of the sensor is an important factor, we provide a faster and less resource demanding alternative approach for the use in a small virus detection device.The execution time of our approach, which can be optimized further using low power hardware such as a digital signal processor (DSP), is at least 2.6 times faster than the current CNN solution while sacrificing only 1 to 2.5 percent points in accuracy.
|
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