Automatic detection of traffic signs is crucial for Advanced Driving Assistance Systems (ADAS). Current two-stage approaches consist of a preliminary object detection step, where the traffic signs are categorized within broader families (e.g., speed limits), and then sub-classes (e.g., speed limit 40). However, these cascading methods fail to achieve satisfying performance, especially in more realistic driving scenarios where images are acquired under more challenging conditions. Under such conditions, the first-stage detection step is likely to provide inaccurate predictions, making the subsequent classification step useless. In this paper, we propose a simple yet effective two-stage fusion framework for traffic sign detection. Different from the previous cascading method, our framework directly predicts categories in the first-stage detection and fuse the two-stage category predictions to improves overall robustness. Besides, in order to filter the false detection boxes under low-resolution inputs, we also propose an effective post-processing method called Surrounding-Aware Non-Maximum Suppression (SA-NMS) as an alternative technique for the first-stage detection. After combining the above proposed methods, our framework obtains good detection performance. Experimental results on the widely used Tsinghua-Tencent 100K (TT100K) traffic sign dataset, which contains images of traffic signs collected under a variety of challenging conditions, show that the proposed framework outperforms current approaches in both accuracy and inference speed, achieving 89.7 mAP and 65 FPS for${608\times608}$low resolution images.

In the recent storage market, Flash-based Solid State Drives (SSDs) have become high-performance alternatives to Hard Disk Drives (HDDs), dramatically increasing SSD shipments. To guarantee product reliability and quality to remain competitive, SSD manufacturers pay significant efforts in technology qualification and reliability design, especially in Manufacturing Self-Test (MST) processes. However, the cost of the MST process becomes more prominent as the memory density of SSD increases. In this paper, we study the MST data in over 20,000 SSDs and propose a novel and economical approach to dynamically reduce the MST overhead by proactive infant defect prediction based on Generative Adversarial Network-Attention based Spatial-Temporal Sequence-to-Sequence network (GAN-ASTSeq). It reduces the temporal cost by 80.2% (i.e., improves the efficiency by 4×) while maintaining an outstanding detection rate of defects.

Approximate query processing (AQP) is one of the key techniques to cope with big data querying problem on account that it obtains approximate answers efficiently. To address non-trivial sample selection and heavy sampling cost issues in AQP, we propose ShadowAQP, an efficient and accurate approach based on attribute-oriented sample size allocation and data generation. We select samples according to group-by and join attributes, and determine the sample size for each group of unique value combinations to improve query accuracy. We design a conditional variational autoencoder model with automatic table data encoding and model update strategies. To further improve accuracy and efficiency, we propose a set of extensions, including parallel multi-round sampling aggregation, data outlier-aware sampling, and dimension reduction optimization. Evaluation results on diversified datasets show that, compared with SOTA approaches, ShadowAQP achieves 5.8× query speed performance improvement on average (up to 12.8×), while reducing query error by 74% on average (up to 95%) at the same time.

In recent years, the design of green datacenters and their enabling technologies, including renewable power managements, have gained a lot of attraction in both industry and academia. However, the maintenance and upgrade of the underlying server system over time (e.g., server replacement due to failures, capacity increases, or migrations), which make datacenters increasingly more heterogeneous in their key processing components (e.g., capacity and variety of processors, memory and storage devices), present a great challenge to optimal allocation of renewable power supply. In other words, the current heterogeneity-unaware power allocation policies have failed to achieve optimal performance given a limited and time varying renewable power supply. In this paper, we propose a dynamic power allocation framework called GreenHetero, which enables adaptive power allocation among heterogeneous servers in green datacenters to achieve the optimal performance when the renewable power varies. Specifically, the GreenHetero scheduler dynamically maintains and updates a performance-power database for each server configuration and workload type through lightweight profiling method. Based on the database and power prediction, the scheduler leverages a well-designed solver to determine the optimal power allocation ratio among heterogeneous servers at runtime. Finally, the power enforcer is used to implement the power source selections and the power allocation decisions. We build an experimental prototype to evaluate GreenHetero. The evaluation shows that our solution can improve the average performance by 1.2x-2.2x and the renewable power utilization by up to 2.7x under tens of representative datacenter workloads compared with the heterogeneity-unaware baseline scheduler.

In this paper, an adaptive continuous sliding mode (SM) control approach is proposed for buck converters by introducing a novel zero-crossing checking mechanism into the twisting algorithm. Instead of the traditional first-order SM approaches, the twisting algorithm can solve their inherent chattering problem and realize the control continuity at the price of unnecessary constant control gain and low precision. Differing from the traditional adaptive mechanisms based on fuzzy logic or Lyapunov stability, the convergence characteristics of the controlled buck converter system are analyzed and further an adaptive twisting algorithm is proposed based on zero-crossing checking online to achieve a time-varying control gain. The number of the zero-crossing checking points can be calculated and the system stability is investigated. Comparative simulations with the traditional twisting algorithm validate the improved algorithm with advantages of high accuracy and excellent performance.

Technology scaling and charge-sharing make nano- scale CMOS latches become severely vulnerable to multiple-node upsets (MNUs). This paper proposes a triple-path dual- interlocked-storage-cell (TPDICE) and soft-error interceptive module (SIM) based 4-Node-Upset (4NU) completely hardened latch, namely 4NUHL latch, that can completely tolerate soft errors, such as 4NUs. The latch mainly consists of 2 TPDICEs and a 3-level SIM which comprises six 2-input C-elements. Owing to the single-node-upset self-recoverability and multiple storage nodes of TPDICEs and the soft-error interception capability of the SIM, the latch can provide complete 4NU tolerance. Simulation results demonstrate that the proposed 4NUHL latch is completely 4NU hardened. Furthermore, we use a high-speed path, clock-gating, and a few transistors to reduce overhead of the proposed latch. We compared the proposed latch with state-of- the-art hardened latches in terms of reliability and overhead to demonstrate the advantages of the proposed latch.

In the era of 'dark silicon', modern data centers have to provision additional hardware resources to guarantee the Quality of Service (QoS) of applications in case of bursty workloads that typically occur in low frequency but high intensity. Fortunately, Computational Sprinting has proven to be an effective approach to boost the computing performance of many-core processor chips, which allows a chip to exceed its power and thermal limits temporarily by turning on all processor cores and absorbing the extra heat dissipation with novel phase-changing materials. Consequently, it offers a promising way to deal with these occasional workload bursts by unleashing the full potentials of hardware, avoiding deploying extra computing resources. In this work, we propose ESprint, a QoS-aware management system based on an effective feedback control mechanism for latency-critical applications in data centers. ESprint can perform computational sprinting by precisely scheduling core count, frequency levels, and sprinting duration, serving bursty workloads without QoS violation under the thermal constraint. Specifically, ESprint effectively predict load intensity in the next time interval, and further dynamically allocates appropriate computing resources to minimize actual power consumption. Our prototype-based evaluation results show that ESprint achieves up to 1.92x improvement on energy efficiency for typical workloads while ensuring QoS, over the non-sprinting strategy. We also explore the design space among energy efficiency, core count/frequency scaling techniques, workload characteristics, burst intensity, and QoS requirements, and draw several key insights to guide the effective use of computational sprinting in data centers.

Computational Sprinting, which allows a chip to exceed its power and thermal limits temporarily by turning on all processor cores and absorbing the extra heat dissipation with certain phase-changing materials, has proven to be an effective way to boost the computing performance for bursty workloads. However, extra power available for sprinting is constrained by existing power distribution infrastructures. Using batteries alone to provide the additional power to achieve performance target not only limits the effectiveness of sprinting, but also negatively impacts the lifetime of the batteries. Leveraging renewable power supply in a green data center provides an opportunity to make full use of Computational Sprinting. However, the intermittent nature of renewable energy, along with limited cooling capacity, makes it very challenging. In this paper, we propose GreenSprint, a renewable energy driven approach that enables a data center to boost its computing performance efficiently by conducting computational sprinting under the intermittent and time-varying nature of renewable energy supply. Three basic strategies are designed to determine the core count and frequency level for sprinting based on current power supply. Furthermore, we propose a Hybrid strategy that combines reinforcement learning to dynamically determine the optimal server setting, targeting at both the power provision safety and the quality of service. In consideration of practical cooling conditions, we also present a thermal-aware sprinting strategy Hybrid-T. Finally, we build an experimental prototype to evaluate GreenSprint on a cluster of 10 servers with a simulated solar power generator. The results show that renewable energy by itself can sustain different duration lengths of sprinting when its supply is sufficient and can improve performance by up to 4.8x for representative interactive applications. We also show the effectiveness of core-count and frequency scaling in the presence of varied renewable power and limited battery energy.

Streaming graph analysis extracts timely insights from evolving graphs, and has gained increasing popularity. For current streaming graph analytics systems, incoming updates are simply cached in a buffer, until being applied onto existing graph structure to construct a new snapshot. Iterative graph algorithms then work on the new snapshot to produce up-to-date analysis result. Nevertheless, we find that for widely used monotonic graph algorithms, the buffered updates can be effectively preprocessed to achieve fast and accurate analysis on new snapshots.

Computational Sprinting has proven to be an effective way to boost the computing performance for bursty workloads, which allows a chip to exceed its power and thermal limits temporarily by turning on all processor cores and absorbing the extra heat dissipation with certain phase-changing materials. However, extra power available for sprinting is constrained by existing power distribution infrastructures. Using batteries alone to provide the additional power to achieve performance target not only limits the effectiveness of sprinting, but also negatively impacts the lifetime of the batteries. Leveraging renewable power supply in a green data center provides an opportunity to exploit the maximal potential of Computational Sprinting. However, the intermittent nature of renewable energy makes it very challenging. In this paper, we propose GreenSprint, a renewable energy driven approach that enables a data center to boost its computing performance efficiently by conducting computational sprinting. We present four sprinting strategies to address the challenge imposed by the intermittent and time-varying nature of renewable energy supply. We build an experimental prototype to evaluate GreenSprint on a cluster of 10 servers with a simulated solar power generator. The results show that renewable energy by itself can sustain different duration lengths of sprinting when its supply is sufficient and can improve performance by up to 4.8x for representative interactive applications. We also show the effectiveness of core-count and frequency scaling in the presence of varied renewable power and limited battery energy.

Distributed graph processing frameworks attempt to eliminate workload imbalance among computing nodes. However, this expectation is generally challenged by underlying heterogeneous nodes and fluctuating graph workloads at runtime. This paper proposes Laro, a graph processing system using dynamic graph repartitioning that collects the actual processing times from all nodes, then reconstructs a vertices distribution with minimal migration costs in every iteration. We manifest that the Variation Coefficient of processing times is a critical metric to quantitatively characterize the workload imbalance among nodes at each iteration. Laro also presents a lazy repartitioning algorithm to improve migration efficiency. Laro has been implemented by extending GPS, a popular repartitioning-featured graph processing system. Our evaluation using real-world graphs shows that, by achieving more balanced workload distributions at runtime, Laro derives maximal speedup of 1.82x and 1.41x over the static Skewed Hash and the dynamic GPS respectively.

In this paper, we propose GreenGear, the first heterogeneous strategy that incorporates wimpy servers into existing green data centers to dynamically deal with power mismatches. Our techniques exploit intelligent green power scheduling policies to provide efficiency-aware power management. We evaluate the GreenGear design on a prototype installed in a test-bed. Compared with a homogeneous server system, GreenGear is able to significantly increase the effective use of the renewable and battery power sources without the supplement of grid power, extending their runtime by 57%, lengthening the UPS lifetime by 2.04X, and improving renewable energy utilization by 51%.

Heterogeneous servers have long been introduced to improve energy efficiency in warehouse-scale computers(WSCs). However, running latency-critical web-services on heterogeneous servers is still challenging because the overheads of transition between such servers heavily impact overall benefits and performance. We propose Montgolfier, a runtime power management system based on a latency-aware feedback control mechanism. It consolidates wimpy and brawny servers into composite nodes to improve energy efficiency while ensuring QoS for latency-critical applications. Montgolfier effectively mitigates the effect of transition overhead between servers with dynamically load prediction and accurately provides thin-provisioned configurations in fine-grain manner for fluctuating loads. Our evaluation results show that Montgolfier reduces energy consumption by up to 34.9% without violating any QoS constraints.