Uncertainty Aware Mapping of Embedded Systems for Reliability, Performance, and Energy

Due to technology downscaling, embedded systems have increased in complexity and heterogeneity. The increasingly large process, voltage, and temperature variations negatively affect the design and optimization process of these systems. These factors contribute to increased uncertainties that in turn undermine the accuracy and effectiveness of traditional design approaches. In this thesis, we formulate the problem of uncertainty aware mapping for multicore embedded system platforms as a multi-objective optimization problem. We present a solution to this problem that integrates uncertainty models as a new design methodology constructed with Monte Carlo and evolutionary algorithms. The solution is uncertainty aware because it is able to model uncertainties in design parameters and to identify robust design points that limit the influence of these uncertainties onto the objective functions. The proposed design methodology is implemented as a tool that can generate the robust Pareto frontier in the objective space formed by reliability, performance, and energy consumption

Investigation of LSTM Based Prediction for Dynamic Energy Management in Chip Multiprocessors

In this paper, we investigate the effectiveness of using long short-term memory (LSTM) instead of Kalman filtering to do prediction for the purpose of constructing dynamic energy management (DEM) algorithms in chip multi-processors (CMPs). Either of the two prediction methods is employed to estimate the workload in the next control period for each of the processor cores. These estimates are then used to select voltage-frequency (VF) pairs for each core of the CMP during the next control period as part of a dynamic voltage and frequency scaling (DVFS) technique. The objective of the DVFS technique is to reduce energy consumption under performance constraints that are set by the user. We conduct our investigation using a custom Sniper system simulation framework. Simulation results for 16 and 64 core network-on-chip based CMP architectures and using several benchmarks demonstrate that the LSTM is slightly better than Kalman filtering

Investigation of LSTM Based Prediction for Dynamic Energy Management in Chip Multiprocessors

In this paper, we investigate the effectiveness of using long short-term memory (LSTM) instead of Kalman filtering to do prediction for the purpose of constructing dynamic energy management (DEM) algorithms in chip multi-processors (CMPs). Either of the two prediction methods is employed to estimate the workload in the next control period for each of the processor cores. These estimates are then used to select voltage-frequency (VF) pairs for each core of the CMP during the next control period as part of a dynamic voltage and frequency scaling (DVFS) technique. The objective of the DVFS technique is to reduce energy consumption under performance constraints that are set by the user. We conduct our investigation using a custom Sniper system simulation framework. Simulation results for 16 and 64 core network-on-chip based CMP architectures and using several benchmarks demonstrate that the LSTM is slightly better than Kalman filtering

C2F2NeUS: Cascade Cost Frustum Fusion for High Fidelity and Generalizable Neural Surface Reconstruction

There is an emerging effort to combine the two popular 3D frameworks using Multi-View Stereo (MVS) and Neural Implicit Surfaces (NIS) with a specific focus on the few-shot / sparse view setting. In this paper, we introduce a novel integration scheme that combines the multi-view stereo with neural signed distance function representations, which potentially overcomes the limitations of both methods. MVS uses per-view depth estimation and cross-view fusion to generate accurate surfaces, while NIS relies on a common coordinate volume. Based on this strategy, we propose to construct per-view cost frustum for finer geometry estimation, and then fuse cross-view frustums and estimate the implicit signed distance functions to tackle artifacts that are due to noise and holes in the produced surface reconstruction. We further apply a cascade frustum fusion strategy to effectively captures global-local information and structural consistency. Finally, we apply cascade sampling and a pseudo-geometric loss to foster stronger integration between the two architectures. Extensive experiments demonstrate that our method reconstructs robust surfaces and outperforms existing state-of-the-art methods.Comment: Accepted by ICCV202

Effect of dietary arginine supplementation on reproductive performance of mice with porcine circovirus type 2 infection

The objective of this study was to investigate whether supplemental dietary arginine increases reproductive performance in mice infected with porcine circovirus type2 (PCV2). A total of 50KM female mice were allotted randomly to the arginine group (0.6% arginine + gestation diet) and control group (1.22% alanine + gestation diet). All the mice began to mate after 14 days of treatment with our prepared feed and challenged with PCV2 at the dose of 100 TCID50 (50% tissue culture infection dose, TCID50) after 7 days of pregnancy. Abortion rate, litter number, litter birth weight, the daily weight gain in the first 7 days and survival rate in the first 2 weeks of the neonates were calculated. The serum progesterone, estrogen, nitric oxide and superoxide dismutase (SOD) activity and total antioxidant capacity (T-AOC) on the 14th day of pregnancy were measured. Arginine supplementation decreased the abortion rate of pregnant mice and mortality of neonates caused by PCV2 infection. Further, litter number, litter birth weight and the daily weight gain of neonates increased in the arginine group compared to the control group. Arginine supplementation increased significantly the serum progesterone (P < 0.01) and nitric oxide levels (P < 0.05), but had little effect on the serum estrogen level. SOD activity and T-AOC in the arginine group were significantly higher (P < 0.01) than the control group. In conclusion, arginine supplementation partially reversed the reproductive failure in mice caused by PCV2 infection

Energy deficiency promotes rhythmic foraging behavior by activating neurons in paraventricular hypothalamic nucleus

BackgroundDysregulation of feeding behavior leads to a variety of pathological manifestations ranging from obesity to anorexia. The foraging behavior of animals affected by food deficiency is not fully understood.MethodsHome-Cage system was used to monitor the behaviors. Immunohistochemical staining was used to monitor the trend of neuronal activity. Chemogenetic approach was used to modify neuronal activity.ResultsWe described here a unique mouse model of foraging behavior and unveiled that food deprivation significantly increases the general activities of mice with a daily rhythmic pattern, particularly foraging behavior. The increased foraging behavior is potentiated by food cues (mouthfeel, odor, size, and shape) and energy deficit, rather than macronutrient protein, carbohydrate, and fat. Notably, energy deficiency increases nocturnal neuronal activity in paraventricular hypothalamic nucleus (PVH), accompanying a similar change in rhythmic foraging behavior. Activating neuronal activity in PVH enhances the amplitude of foraging behavior in mice. Conversely, inactivating neuronal activity in PVH decreases the amplitude of foraging behavior and impairs the rhythm of foraging behavior.DiscussionThese results illustrate that energy status and food cues regulate the rhythmic foraging behavior via PVH neuronal activity. Understanding foraging behavior provides insights into the underlying mechanism of eating-related disorders

Pneumonia Incidence and Mortality in Mainland China: Systematic Review of Chinese and English Literature, 1985–2008

BACKGROUND: Pneumonia is a leading infectious disease killer worldwide, yet the burden in China is not well understood as much of the data is published in the non-English literature. METHODOLOGY/PRINCIPAL FINDINGS: We systematically reviewed the Chinese- and English-language literature for studies with primary data on pneumonia incidence and mortality in mainland China. Between 1985 and 2008, 37 studies met the inclusion criteria. The quality of the studies was highly variable. For children <5 years, incidence ranged from 0.06-0.27 episodes per person-year and mortality ranged from 184-1,223 deaths per 100,000 population. Overall incidence and mortality were stable or decreased over the study period and were higher in rural compared to urban areas. CONCLUSIONS/SIGNIFICANCE: Pneumonia continues to be a major public health challenge in young children in China, and estimates of pneumonia incidence and mortality vary widely. Reliable surveillance data and new prevention efforts may be needed to achieve and document additional declines, especially in areas with higher incidence and mortality such as rural settings

Algorithm-Architecture-Hardware Co-Design in Computing Systems: From Chip Multicore to the Cloud

The computational demands for training deep learning models doubled every three months recently. However, according to Moore’s Law, the computational power available only doubled every two years. To bridge this demand-supply gap while optimizing energy consumption and carbon emission, through my dissertation, we propose a novel algorithm-architecture-hardware co-design cross-layer approach for computing systems: from chip multicore to the cloud. At the Chip Multicore Level: How can we design high-performance network-on-chip based multiprocessors that are reliable to uncertainty in design parameters? This dissertation answered this question by 1) laying the foundation for uncertainty modeling and robust multi-objective optimization for embedded systems design and 2) providing computer-aided design (CAD) automation tools, which incorporate a novel design method to achieve this multi-level goal. Chapter 3 proposed the first uncertainty aware reliability model for NoC based chip multicore; it integrated uncertainty models as a new design methodology. Chapter 4, for the first time, applied the info-gap theory to uncertainty modeling in the context of embedded systems design. We developed uncertainty-aware and reliability-oriented CAD tools that can identify the most robust design solutions that compose the 3D Pareto frontier. This has not been done before. We demonstrated that significant differences between actual values and estimations of design attributes exist when uncertainty in design parameters is considered. At the Server and Cluster Levels: How should we build generic and effective machine learning models to improve datacenter scheduling algorithms? This dissertation answered this question by using deep learning models within a unified hierarchical approach for scheduling that combines cluster and node levels scheduling while modeling interference and heterogeneity and considering performance and energy usage as design objectives. Chapter 5 combines a unified approach cluster and node level scheduling algorithms, and it can consider specific optimization objectives including job completion time, energy usage, and energy delay product (EDP). Experimental results demonstrated that this approach outperforms state-of-the-art schedulers from industry and academia by 41.98% in energy delay product (EDP), 38.65% in energy usage, and 10.2% in job completion time. Chapter 6 harnesses additional external knowledge about applications and servers and exploits simplicity to develop AI-assisted datacenter scheduling

Reliability Optimization Under Severe Uncertainty for NoC Based Architectures Using an Info-Gap Decision Approach

Increased uncertainties in design parameters undermine the accuracy of the mapping of embedded applications to Network-on-Chip (NoC) based manycore architectures. In this paper, we attempt for the first time to apply the info-gap theory to uncertainty modeling in the context of embedded systems design. We first propose a novel info-gap based uncertainty-aware reliability model for NoC based manycore platforms. We then develop an uncertainty-aware solution to the problem of mapping in embedded systems. The solution is implemented as a computer program that can generate robust Pareto frontiers. Simulation results indicate that the proposed info-gap based uncertainty-aware mapping generates Pareto frontiers that have significant differences from the ones obtained with a traditional deterministic approach. Identifying and quantifying these differences is an important first step towards the development of better mapping optimization processes in order to arrive to optimal rather than suboptimal solutions

Quantifying the Impact of Uncertainty in Embedded Systems Mapping for NoC Based Architectures

We describe a modeling framework to capture and account for uncertainty in design parameters in embedded systems. We then develop an uncertainty-aware solution to the problem of mapping in embedded systems that uses Network-on-Chip (NoC) based architecture platforms. The problem of mapping is formulated as a multi-objective - reliability, performance, and energy consumption - optimization problem. To solve this problem, we propose a solution based on the NSGA-II genetic algorithm and Monte Carlo simulation techniques. The solution is implemented as a computer-aid design tool that can generate robust 3D Pareto frontiers in the solution space formed by the design objectives of reliability, performance, and energy consumption. Comparison to several state-of-the-art models and solutions for the mapping problem, indicate that significant differences in the actual values of the design attribute of interest exist when one considers uncertainty in design parameters. For example, in the case of mapping with reliability as the only objective, 10% uncertainty in design parameters can lead to a 10.06% difference in MTTF estimation. In the case of mapping with execution time and energy consumption as objectives, the difference in 2D Pareto frontiers due to 10% uncertainty in design parameters can be up to 7.9%. These differences are important because they can mislead the overall optimization process of mapping toward suboptimal solution points. The DESUU-NOC tool that implements the proposed multi-objective mapping algorithm has as a main feature and contribution of this paper the ability to generate 3D Pareto frontiers comprised of robust solution points