1,721,008 research outputs found
Playing with two fires at the border: Geopolitics of wildfires in the inter-Korean border
No full textSnap-through oscillations of tandem elastic sheets in uniform flow
No full textFor smart system designs based on elastic structures, buckled elastic sheets where both edges are clamped have gained significant attention as they exhibit interesting dynamic behaviors, namely, snap-through motion. In this study, the critical conditions and post-equilibrium responses of tandem buckled sheets under unbounded uniform flow are investigated experimentally to understand the mutual interactions of the two sheets for potential applications as novel energy harvesting systems. The critical velocity at which they initiate snap-through oscillations from an equilibrium state is examined by varying the gap distance and initial buckled shape of the tandem sheets. The dynamic characteristics of the oscillation state, such as the amplitude, frequency, and phase difference, are examined with a particular focus on comparing the behaviors of the two sheets. Regardless of the gap distance, free-stream velocity, and initial buckled shape, the front and rear sheets exhibit similar deformation magnitudes and bending energies under repeated snap-throughs, which is notably different from flapping flag models with a free end. © 2021 Elsevier Ltd
Flow-induced periodic snap-through dynamics
No full textThe stability and post-critical behaviour of periodic snapping are investigated experimentally for a buckled elastic sheet with two clamped ends under an external uniform flow. In addition to experimental investigations, low-order numerical simulations are conducted with the elastica model for the deformation of the sheet, which is coupled with the simple quasi-steady fluid force model based on Bollay's lift theory, in order to identify the deformed shape of the sheet in an equilibrium state and the critical velocity where the sheet begins to snap. Continuous exposure to fluid-dynamic loading induces snap-through oscillations from an initial equilibrium state. While the critical flow velocity for bifurcation is inversely related to the ratio of the streamwise distance of the sheet to its length, it is not significantly affected by the mass ratio of the sheet and the surrounding fluid, leading to divergence instability. In the post-equilibrium state, regular oscillations with the same dominant modes persist in the sheet for a broad range of the flow velocity. As the sheet crosses the midline in the snapping process, the bending energy stored in the sheet is released quickly, and the time for energy release is found to be lower than that required for energy storage. Because of the initial buckled shape, the minimum bending energy of the sheet over a cycle remains at least 40% of its maximum magnitude.
Design of Processing-in-Memory With Triple Computational Path and Sparsity Handling for Energy-Efficient DNN Training
No full textAs machine learning (ML) and artificial intelligence (AI) have become mainstream technologies, many accelerators have been proposed to cope with their computation kernels. However, they access the external memory frequently due to the large size of deep neural network model, suffering from the von Neumann bottleneck. Moreover, as privacy issue is becoming more critical, on-device training is emerging as its solution. However, on-device training is challenging because it should perform the training under a limited power budget, which requires a lot more computations and memory accesses than the inference. In this paper, we present an energy-efficient processing-in-memory (PIM) architecture supporting end-to-end on-device training named T-PIM. Its macro design includes an 8T-SRAM cell-based PIM block to compute in-memory AND operation and three computational datapaths for end-to-end training. Each of three computational paths integrates arithmetic units for forward propagation, backward propagation, and gradient calculation and weight update, respectively, allowing the weight data stored in the memory stationary. T-PIM also supports variable bit precision to cover various ML scenarios. It can use fully variable input bit precision and 2-bit, 4-bit, 8-bit, and 16-bit weight bit precision for the forward propagation and the same input bit precision and 16-bit weight bit precision for the backward propagation. In addition, T-PIM implements sparsity handling schemes that skip the computation for input data and turn off the arithmetic units for weight data to reduce both unnecessary computations and leakage power. Finally, we fabricate the T-PIM chip on a 5.04mm(2) die in a 28-nm CMOS logic process. It operates at 50-280MHz with the supply voltage of 0.75-1.05V, dissipating 5.25-51.23mW power in inference and 6.10-37.75mW in training. As a result, it achieves 17.90-161.08TOPS/W energy efficiency for the inference of 1-bit activation and 2-bit weight data, and 0.84-7.59TOPS/W for the training of 8-bit activation/error and 16-bit weight data. In conclusion, T-PIM is the first PIM chip that supports end-to-end training, demonstrating 2.02 times performance improvement over the latest PIM that partially supports training.
Beyond the Data Imbalance: Employing the Heterogeneous Datasets for Vehicle Maneuver Prediction
Full text linkPredicting the maneuvers of surrounding vehicles is imperative for the safe navigation of autonomous vehicles. However, naturalistic driving datasets tend to be highly imbalanced, with a bias towards the “going straight” maneuver. Consequently, learning and accurately predicting turning maneuvers pose significant challenges. In this study, we propose a novel two-stage maneuver learning method that can overcome such strong biases by leveraging two heterogeneous datasets in a complementary manner. In the first training phase, we utilize an intersection-centric dataset characterized by balanced distribution of maneuver classes to learn the representations of each maneuver. Subsequently, in the second training phase, we incorporate an ego-centric driving dataset to account for various geometrical road shapes, by transferring the knowledge of geometric diversity to the maneuver prediction model. To facilitate this, we constructed an in-house intersection-centric trajectory dataset with a well-balanced maneuver distribution. By harnessing the power of heterogeneous datasets, our framework significantly improves maneuver prediction performance, particularly for minority maneuver classes such as turning maneuvers. The dataset is available at https://github.com/KAIST-VDCLab/VDC-Trajectory-Dataset
DFX: A Low-latency Multi-FPGA Appliance for Accelerating Transformer-based Text Generation
No full textTransformer is a deep learning language model widely used for natural language processing (NLP) services in datacenters. Among transformer models, Generative Pretrained Transformer (GPT) has achieved remarkable performance in text generation, or natural language generation (NLG), which needs the processing of a large input context in the summarization stage, followed by the generation stage that produces a single word at a time. The conventional platforms such as GPU are specialized for the parallel processing of large inputs in the summarization stage, but their performance significantly degrades in the generation stage due to its sequential characteristic. Therefore, an efficient hardware platform is required to address the high latency caused by the sequential characteristic of text generation. In this paper, we present DFX, a multi-FPGA acceleration appliance that executes GPT-2 model inference end-to-end with low latency and high throughput in both summarization and generation stages. DFX uses model parallelism and optimized dataflow that is model-and-hardware-aware for fast simultaneous workload execution among devices. Its compute cores operate on custom instructions and provide GPT-2 operations end-to-end. We implement the proposed hardware architecture on four Xilinx Alveo U280 FPGAs and utilize all of the channels of the high bandwidth memory (HBM) and the maximum number of compute resources for high hardware efficiency. DFX achieves 5.58 × speedup and 3.99 × energy efficiency over four NVIDIA V100 GPUs on the modern GPT-2 model. DFX is also 8.21 × more cost-effective than the GPU appliance, suggesting that it is a promising solution for text generation workloads in cloud datacenters
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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