System-Level Performance and Power Optimization for Memory and Storage Subsystems in Smart Personal Devices

隨著行動運算能力的快速提升，各式各樣的應用程式在個人裝置上變得可能。個人裝置的演進對於記憶體與儲存裝置子系統帶來了新的挑戰。當多個應用程式同時執行時，應用程式之間的記憶體競爭延遲了應用程式記憶體存取的時間史記憶體牆(Memory wall) 的問題更加惡化。記憶體存取時間的延遲使得運算處理單元因為等待記憶體而帶來了不必要的靜態功率(leakage power) 的浪費。為了達到行動裝置的高可靠度，應用程式產生大量的儲存裝置同步寫入(Synchronous write)使系統中的緩衝快取的效益大打折扣進而降低個人裝置的使用者經驗。在這論文之中，我們專注於記憶體與儲存裝置子系統的最佳化。對於記憶體系統，我們提出了一個階層式記憶體排程器(Hierarchical memory scheduler)，用來減少來自不同應用程式的記憶體存取的衝擊並達到公平性的保證。為了減少運算處理單元因為長時間等待記憶體回應造成的靜態功率的浪費，我們提出了一個記憶體存取感知電源門控機制(memory access aware power gating) 得以達到更精確的電源門控判定與控制。對於儲存裝置系統，我們提出了一個階層式DRAM 與PCM 緩衝快取(Hierarchical DRAM and PCM buffer cache) 用來減少儲存裝置同步寫入的衝擊。基於此階層式緩衝快取架構，我們提出了多個緩衝快取管理機制進而提升個人裝置的使用者經驗。As the mobile computing speed rapidly grows, a variety of applications in the personal devices have been made possible. The evolution of personal devices presents new challenges on the memory and storage subsystems. When multiple applications run together, the memory contention among applications prolongs memory access time, which can exacerbate the long-existing memory wall problem. The long memory access latency also results in unnecessary leakage waste of the processing unit due to the memory stall. To provide high reliability for the mobile devices, applications generate bulk storage synchronous writes, which reduces the benefits of the DRAM buffer cache and degrades the user experience of the personal devices. In this thesis, we focus on the optimization for both memory and storage subsystems in smart personal devices. For the memory system, we propose a hierarchical memory scheduler to reduce the impacts of concurrent memory accesses from different applications with fairness guarantee. To reduce the leakage waste of the processing units due to the long memory latency, we propose a memory access aware power gating mechanism which makes the power gating decisions to those units judiciously. For the storage system, we adopt a hierarchical DRAM and PCM buffer cache to reduce the impacts of synchronous storage writes. Based on the hierarchical buffer cache architecture, we propose the buffer cache management policies to improve the user experience of the personal devices

A Hybrid DRAM/PCM Buffer Cache Architecture for Smartphones with QoS Consideration

Flash memory is widely used in mobile phones to store contact information, application files, and other types of data. In an operating system, the buffer cache keeps the I/O blocks in dynamic random access memory (DRAM) to reduce the slow flash accesses. However, in smartphones, we observed two issues which reduce the benefits of the buffer cache. First, a large number of synchronous writes force writing the data from the buffer cache to flash frequently. Second, the large amount of I/O accesses from background applications diminishes the buffer cache efficiency of the foreground application, which degrades the quality-of-service (QoS). In this article, we propose a buffer cache architecture with hybrid DRAM and phase change memory (PCM) memory, which improves the I/O performance and QoS for smartphones. We use a DRAM first-level buffer cache to provide high buffer cache performance and a PCM last-level buffer cache to reduce the impact of frequent synchronous writes. Based on the proposed hierarchical buffer cache architecture, we propose a sub-block management and background flush to reduce the impact of the PCM write limitation and the dirty block write-back overhead, respectively. To improve the QoS, we propose a least-recently-activated first replacement policy (LRA) to keep the data from the applications that are most likely to become the foreground one. The experimental results show that with the proposed mechanisms, our hierarchical buffer cache can improve the I/O response time by 20% compared to the conventional buffer cache. The proposed LRA can improve the foreground application performance by 1.74x compared to the conventional CLOCK policy.</jats:p

Applying Machine Learning Technologies Based on Historical Activity Features for Multi-Resident Activity Recognition

Due to the aging population, home care for the elderly has become very important. Currently, there are many studies focusing on the deployment of various sensors in the house to recognize the home activities of the elderly, especially for the elderly living alone. Through these, we can detect the home situation of the single person and ensure his/her living safety. However, the living environment of the elderly includes, not only the person living alone, but also multiple people living together. By applying the traditional methods for a multi-resident environment, the “individual” activities of each person could not be accurately identified. This resulted in an inability to distinguish which person was involved in what activities, and thus, failed to provide personal care. Therefore, this research tries to investigate how to recognize home activities in multi-resident living environments, in order to accurately distinguish the association between residents and home activities. Specifically, we propose to use the special characteristics of historical activity of residents in a multi-person environment, including activity interaction, activity frequency, activity period length, and residential behaviors, and then apply a suite of machine learning methods to train and test. Five traditional models of supervised learning and two deep learning methods are explored to tackle this problem. Through the experiments with real datasets, the proposed methods were found to achieve higher precision, recall and accuracy with less training time. The best accuracy can reach up to 91% and 95%, by J48DT, and LSTM, respectively, in different living environments.</jats:p

Applying Machine Learning Technologies Based on Historical Activity Features for Multi-Resident Activity Recognition

Due to the aging population, home care for the elderly has become very important. Currently, there are many studies focusing on the deployment of various sensors in the house to recognize the home activities of the elderly, especially for the elderly living alone. Through these, we can detect the home situation of the single person and ensure his/her living safety. However, the living environment of the elderly includes, not only the person living alone, but also multiple people living together. By applying the traditional methods for a multi-resident environment, the “individual” activities of each person could not be accurately identified. This resulted in an inability to distinguish which person was involved in what activities, and thus, failed to provide personal care. Therefore, this research tries to investigate how to recognize home activities in multi-resident living environments, in order to accurately distinguish the association between residents and home activities. Specifically, we propose to use the special characteristics of historical activity of residents in a multi-person environment, including activity interaction, activity frequency, activity period length, and residential behaviors, and then apply a suite of machine learning methods to train and test. Five traditional models of supervised learning and two deep learning methods are explored to tackle this problem. Through the experiments with real datasets, the proposed methods were found to achieve higher precision, recall and accuracy with less training time. The best accuracy can reach up to 91% and 95%, by J48DT, and LSTM, respectively, in different living environments

Bearing vibration detection and analysis using enhanced fast Fourier transform algorithm

It is known that the vibration impulses occurred from a bearing defect are non-periodic but cyclostationary due to the slippage of rollers. The vibration status is often perceived to be synonymous with quality and thus used for predictive maintenance before breakdown. As a result, the analysis of vibration has been used as a key condition tool for fault detection, diagnosis, and prognosis. Any defect in a bearing causes some vibration that consists of certain frequencies depending on the nature and location of the defect. Although many techniques for time–frequency analysis are reported to measure vibration signals, they were found less efficient in practical applications. For this reason, this article develops an on-line bearing vibration detection and analysis using enhanced fast Fourier transform algorithm. The relation between major vibration frequency and dispersed leakage caused from fast Fourier transform can be induced, and it is then used to establish a mathematical model to find major frequencies of vibration signal. Also, the dispersed energy can be collected to retrieve its original gravitational acceleration. The proposed model is developed using a simple arithmetic operation based on fast Fourier transform so that it is feasible for more efficient calculation in impulse signal analysis. Both measurement calibration and practical results verify that the proposed scheme can achieve accurate, rapid, and reliable outcomes. </jats:p