| Mikail Yayla.
Resource-Aware Acceleration for Nano Particle Classification.
Master's Thesis, 2019
[BibTeX][PDF]@mastersthesis { 2019-yayla,
title = {Resource-Aware Acceleration for Nano Particle Classification},
author = {Yayla, Mikail},
school = {TU Dortmund},
year = {2019},
file = {https://ls12-joomla-host.cs.tu-dortmund.de/daes/media/documents/theses/2019-yayla.pdf},
confidential = {n},
adviser = {Dr. Kuan-Hsun Chen},
} |
| Christian Hakert, Kuan-Hsun Chen, Mikail Yayla, Georg von der Brüggen, Sebastian Bloemeke and Jian-Jia Chen.
Software-Based Memory Analysis Environments for In-Memory Wear-Leveling.
In 25th Asia and South Pacific Design Automation Conference ASP-DAC 2020, Invited Paper
Beijing, China, 2019
[BibTeX][Abstract]@inproceedings { nvmsimulator,
author = {Hakert, Christian and Chen, Kuan-Hsun and Yayla, Mikail and Br\"uggen, Georg von der and Bloemeke, Sebastian and Chen, Jian-Jia},
title = {Software-Based Memory Analysis Environments for In-Memory Wear-Leveling},
booktitle = {25th Asia and South Pacific Design Automation Conference ASP-DAC 2020, Invited Paper},
year = {2019},
address = {Beijing, China},
keywords = {kuan, nvm-oma, georg},
confidential = {n},
abstract = {Emerging non-volatile memory (NVM) architectures are considered as a replacement for DRAM and storage in the near future, since NVMs provide low power consumption, fast access speed, and low unit cost. Due to the lower write-endurance of NVMs, several in-memory wear-leveling techniques have been studied over the last years. Since most approaches propose or rely on specialized hardware, the techniques are often evaluated based on assumptions and in-house simulations rather than on real systems. To address this issue, we develop a setup consisting of a gem5 instance and an NVMain2.0 instance, which simulates an entire system (CPU, peripherals, etc.) together with an NVM plugged into the system. Taking a recorded memory access pattern from a low-level simulation into consideration to design and optimize wear-leveling techniques as operating system services allows a cross-layer design of wear- leveling techniques. With the insights gathered by analyzing the recorded memory access patterns, we develop a software-only wear-leveling solution, which does not require special hardware at all. This algorithm is evaluated afterwards by the full system simulation.},
}
Emerging non-volatile memory (NVM) architectures are considered as a replacement for DRAM and storage in the near future, since NVMs provide low power consumption, fast access speed, and low unit cost. Due to the lower write-endurance of NVMs, several in-memory wear-leveling techniques have been studied over the last years. Since most approaches propose or rely on specialized hardware, the techniques are often evaluated based on assumptions and in-house simulations rather than on real systems. To address this issue, we develop a setup consisting of a gem5 instance and an NVMain2.0 instance, which simulates an entire system (CPU, peripherals, etc.) together with an NVM plugged into the system. Taking a recorded memory access pattern from a low-level simulation into consideration to design and optimize wear-leveling techniques as operating system services allows a cross-layer design of wear- leveling techniques. With the insights gathered by analyzing the recorded memory access patterns, we develop a software-only wear-leveling solution, which does not require special hardware at all. This algorithm is evaluated afterwards by the full system simulation.
|
| 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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