@inproceedings { ipsndemo2020,
  author = {Huang, Yunfeng and Wu, Fang-Jing and Hakert, Christian and Br\"uggen, Georg and Chen, Kuan-Hsun and Chen, Jian-Jia and B\"ocker, Patrick and Chernikov, Petr and Cruz, Luis and Duan, Zeyi and Gheith, Ahmed and Yantao Gong, Anand Gopalan and Prakash, Karthik and Tauqir, Ammar and Wang, Yue},
  title = {Demo Abstract: Perception vs. Reality - Never Believe in What You See},
  booktitle = {19th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)},
  year = {2020 },
  address = {Virtual Conference},
  keywords = {kuan, georg},
  confidential = {n},
  abstract = {The increasing availability of heterogeneous ambient sensing systems challenges the according information processing systems to analyse and compare a variety of different systems in a single scenario. For instance, localization of objects can be performed by image processing systems as well as by radio based localization. If such systems are utilized to localize the same objects, synergy of the outputs is important to enable comparable and meaningful analysis.This demo showcases the practical deployment and challenges ofsuch an example system.},
}

@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 = {2020},
  address = {Beijing, China},
  keywords = {kuan, nvm-oma, georg},
  file = {https://ls12-www.cs.tu-dortmund.de/daes/media/documents/publications/downloads/2020-aspdac-nvm.pdf},
  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.},
}

@inproceedings { hakert2020nvmsa,
  author = {Hakert, Christian and Chen, Kuan-Hsun and Kuenzer, Simon and Santhanam, Sharan and Chen, Shuo-Han and Chang, Yuan-Hao and Huici, Felipe and Chen, Jian-Jia},
  title = {Split’n Trace NVM: Leveraging Library OSes for Semantic Memory Tracing},
  booktitle = {9th Non-Volatile Memory Systems and Applications Symposium (NVMSA)},
  year = {2020},
  address = {Virtual Conference},
  keywords = {kuan, nvm-oma, },
  confidential = {n},
  abstract = {With the rise of non-volatile memory (NVM) as a replacement for traditional main memories (e.g. DRAM), memory access analysis is becoming an increasingly important topic. NVMs suffer from technical shortcomings as such as reduced cell endurance which call for precise memory access analysis in order to design maintenance strategies that can extend the memory’s lifetime. While existing memory access analyzers trace memory accesses at various levels, from the application level with code instrumentation, down to the hardware level where software is executed on special analysis hardware, they usually interpret main memory as a consecutive area, without investigating the application semantics of different memory regions.
In contrast, this paper presents a memory access simulator, which splits the main memory into semantic regions and enriches the simulation result with semantics from the analyzed application. We leverage a library-based operating system called Unikraft by ascribing memory regions of the simulation to the relevant OS libraries. This novel approach allows us to derive a detailed analysis of which libraries (and thus functionalities) are responsible for which memory access patterns. Through offline profiling with our simulator, we provide a fine-granularity analysis of memory access patterns that provide insights for the design of efficient NVM maintenance strategies.},
}

@article { hakert2020softwear,
  author = {Hakert, Christian and Chen, Kuan-Hsun and Genssler, Paul R. and Br\"uggen, Georg and Bauer, Lars and Amrouch, Hussam and Chen, Jian-Jia and Henkel, J\"org},
  title = {SoftWear: Software-Only In-Memory Wear-Leveling for Non-Volatile Main Memory},
  journal = {CoRR},
  year = {2020},
  volume = {abs/2004.03244},
  url = {https://arxiv.org/pdf/2004.03244.pdf},
  keywords = {kuan, nvm-oma, georg},
  confidential = {n},
  abstract = {Several emerging technologies for byte-addressable non-volatile memory (NVM) have been considered to replace DRAM as the main memory in computer systems during the last years. The disadvantage of a lower write endurance, compared to DRAM, of NVM technologies like Phase-Change Memory (PCM) or Ferroelectric RAM (FeRAM) has been addressed in the literature. As a solution, in-memory wear-leveling techniques have been proposed, which aim to balance the wear-level over all memory cells to achieve an increased memory lifetime. Generally, to apply such advanced aging-aware wear-leveling techniques proposed in the literature, additional special hardware is introduced into the memory system to provide the necessary information about the cell age and thus enable aging-aware wear-leveling decisions.
This paper proposes software-only aging-aware wear-leveling based on common CPU features and does not rely on any additional hardware support from the memory subsystem. Specifically, we exploit the memory management unit (MMU), performance counters, and interrupts to approximate the memory write counts as an aging indicator. Although the software-only approach may lead to slightly worse wear-leveling, it is applicable on commonly available hardware. We achieve page-level coarse-grained wear-leveling by approximating the current cell age through statistical sampling and performing physical memory remapping through the MMU. This method results in non-uniform memory usage patterns within a memory page. Hence, we further propose a fine-grained wear-leveling in the stack region of C / C++ compiled software.
By applying both wear-leveling techniques, we achieve up to 78.43% of the ideal memory lifetime, which is a lifetime improvement of more than a factor of 900 compared to the lifetime without any wear-leveling. },
}

@article { buschjger2020explainable,
  author = {Buschj\"ager, Sebastian and Chen, Jian-Jia and Chen, Kuan-Hsun and G\"unzel, Mario and Hakert, Christian and Morik, Katharina and Novkin, Rodion and Pfahler, Lukas and Yayla, Mikail},
  title = {Towards Explainable Bit Error Tolerance of Resistive RAM-Based Binarized Neural Networks},
  journal = {CoRR},
  year = {2020},
  volume = {abs/2002.00909},
  url = {https://arxiv.org/pdf/2002.00909.pdf},
  keywords = {kuan, nvm-oma},
  confidential = {n},
  abstract = {Non-volatile memory, such as resistive RAM (RRAM), is an emerging energy-efficient storage, especially for low-power machine learning models on the edge. It is reported, however, that the bit error rate of RRAMs can be up to 3.3% in the ultra low-power setting, which might be crucial for many use cases. Binary neural networks (BNNs), a resource efficient variant of neural networks (NNs), can tolerate a certain percentage of errors without a loss in accuracy and demand lower resources in computation and storage. The bit error tolerance (BET) in BNNs can be achieved by flipping the weight signs during training, as proposed by Hirtzlin et al., but their method has a significant drawback, especially for fully connected neural networks (FCNN): The FCNNs overfit to the error rate used in training, which leads to low accuracy under lower error rates. In addition, the underlying principles of BET are not investigated. In this work, we improve the training for BET of BNNs and aim to explain this property. We propose straight-through gradient approximation to improve the weight-sign-flip training, by which BNNs adapt less to the bit error rates. To explain the achieved robustness, we define a metric that aims to measure BET without fault injection. We evaluate the metric and find that it correlates with accuracy over error rate for all FCNNs tested. Finally, we explore the influence of a novel regularizer that optimizes with respect to this metric, with the aim of providing a configurable trade-off in accuracy and BET.},
}

@mastersthesis { HakertMA,
  title = {Memory Access Analysis and Endurance Leveling Approaches for Non-volatile Working Memory Systems},
  author = {Hakert, Christian},
  school = {TU Dortmund},
  year = {2019},
  keywords = {nvm-oma},
  file = {https://ls12-www.cs.tu-dortmund.de/daes/media/documents/theses/2019-hakert.pdf},
  confidential = {n},
  abstract = {Emerging technologies for non-volatile memory (NVM), such as phase-change memory (PCM), ferroelectric RAM (FeRAM), spin-transfer torque magnetoresistive RAM (STT-MRAM) and many more, have been considered as a replacement for DRAM and storage due to low leakage power, high capacity and fast access times. A major disadvantage of some NVMs is the significantly lower write endurance compared to DRAM. To tackle this issue, several in-memory wear-leveling approaches have been proposed in literature, targeting the program execution to make the memory write usage more uniform. These approaches operate on different granularities, like memory pages, cache-lines, big segments, etc. Aging-aware approaches take the current wear-level of memory cells into account to make the best wear-leveling decisions, while other approaches try to balance the wear-level in a random based manner or in a circular manner. However, most approaches propose specialized hardware controllers to collect the aging information and to perform wear-leveling actions, like relocations of memory regions.
As specialized hardware may be hard to build at all, this thesis proposes so-called software only wear-leveling, which only makes use of commonly available hardware components. Aging-aware wear-leveling on the granularity of virtual memory pages is achieved by estimating the cell aging by a statistical runtime approximation. As an extension to this, fine-grained wear-leveling for the stack region of applications is achieved by relocating the stack periodically in a circular manner through a reserved memory region.
The necessity for commonly available hardware components only allows the techniques to be executed in a full system simulation setup (i.e. gem5 as a simulator for an ARM CPU and NVMain 2.0 as a simulator for the NVM) for evaluation purposes. The results show that coarse-grained aging-aware wear-leveling with approximated cell ages works out, but cannot resolve non-uniform write distributions within virtual memory pages. This limits the gained improvement of the memory lifetime up to a factor of ≈ 13 compared to the baseline without any wear-leveling. Combining the coarse-grained approach with the fine- grained stack wear-leveling technique, the non-uniformity caused by the stack region can be resolved and the evaluations result in an improvement of the memory lifetime up to a factor of ≈ 900 compared to the baseline.},
  adviser = {Prof. Dr. Jian-Jia Chen and Dr. Kuan-Hsun Chen},
}

@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},
  file = {https://ls12-www.cs.tu-dortmund.de/daes/media/documents/publications/downloads/mlcad_2019.pdf},
  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.},
}